软地面智能气垫车自主导航研究
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摘要
本文将非路面车辆和机器人学的相关技术相结合,对软地面智能气垫车自主导航问题进行了研究。气垫车能够利用垫升系统承担部分载荷,减小行走机构的接地压强,因此可以提高在沼泽、滩涂、沙漠等软地面环境下的通过性。辅以自主导航和自动控制技术,智能气垫车可自行开展诸如探险、搜救、清污、采样的危险性工作,保证了作业人员的安全。因此,本研究在石油业、矿业、运输业、农业等众多生产部门及军事领域具有应用价值。
本文将智能气垫车自主导航体系抽象为气垫车、真实环境和虚拟环境三大系统,将自主导航的工作流程归纳为基于传感器信息的真实环境识别和虚拟环境构建,基于虚拟环境的车辆运动规划,以及用以实现规划目标的车辆运行控制。因此,全文依次研究了气垫车建模、土壤参数估值、能耗优化、运动规划和运行控制。
首先,结合原理样车,建立了气垫车的结构模型和动力学模型。气垫车的主要结构包括垫升系统和驱动系统,针对垫升系统建立了其压强和流量的损失模型,针对驱动系统建立了其运动传递模型。此外,还利用拉格朗日法建立了11自由度车辆动力学模型。接下来,作为软地面环境识别的重要部分,对8个关键土壤参数进行了分步估值,依次利用g-EKF联合算法计算推力相关土壤参数,利用对数均值法计算垂向力相关土壤参数,利用最小二乘法计算推土阻力相关土壤参数。试验结果表明,分步估值算法能够消除估值多解问题,提高估值准确性。
继而进行了能耗优化研究。分析了气垫车运行参数(如载荷分配比、滑转率和车速等)之间的关系,将总能耗简化表示为载荷分配比和滑转率的函数,并设计了自适应蜜蜂算法进行参数寻优。优化的结果可作为运行参数的稳态目标值,用于后续运动规划和运行控制。试验结果表明,由于自适应蜜蜂算法具有功能划分、并行计算和局部搜索范围自适应调节的特点,它能够同时提高优化准确性和优化效率。
继而利用人工势场法对气垫车进行了运动规划,计算自主导航过程中其平动速度和转动速度的瞬态目标值。在建立势场时,多种运动要求以目标物吸引势、障碍物排斥势、动力学安全势和能耗经济势的形式予以表现。在运用势场时,对车速方向和速率进行分步规划,以提高计算效率。试验结果表明,在不同的工况下,各势函数项有不同的相对重要性,运动规划结果使当时主要的运动要求得以满足。
最后研究了气垫车的运行控制。采用以模糊PID控制器为核心的并行双环控制系统控制车速和载荷分配比,模糊推理规则的设计体现出参数协调控制的思想。相对而言,车速控制跟踪瞬态目标值,因而更加注重动态性能;载荷分配比控制跟踪稳态目标值,因而更加注重静态性能。仿真试验结果表明,在控制系统的作用下,气垫车能够顺利抵达目的地并实现软着陆,其行驶路径和速度的变化符合理论分析结果;轮速控制表现出良好的动态性能,有利于提高车辆的行驶安全性;载荷分配比控制表现出良好的静态性能,有利于降低车辆能耗。
本文虽以软地面智能气垫车作为研究对象,但其研究成果可向多个领域推广。例如,设计的土壤参数在线分步估值算法是一种针对气垫车辆的普适算法;自适应蜜蜂算法作为一种启发式全局优化算法,可用于一般的函数优化问题;改进的人工势场法在势函数构成、多种运动要求的协调以及运动规划的实施等方面均有所创新,可用于智能车辆和机器人的运动规划。上述三项技术也因此构成本文的创新点。
By combining the techniques in the fields of off-road vehicle and robotics, this work studies the autonomous navigation problem for intelligent air-cushion vehicles (ACVs) for soft terrain. ACVs are able to use the lifting system to adjust the pressure under the travelling mechanism and thus able to improve the crossing ability on soft terrain, such as swamp, beach and desert. By taking advantage of autonomous navigation and automatic control techniques, intelligent ACVs may substitute people in dangerous tasks like exploration, searching, cleaning and sampling. Consequently, this interdisciplinary study could have practical significance in many applications in industry, agriculture and military.
The autonomous navigation system of the targeted intelligent ACV is described with three sub-systems, namely, the ACV, a real environment, and a virtual environment. Its workflow is briefly summarized as the real environment identification and the virtual environment construction based on sensor information, vehicle motion planning implemented in the virtual environment, as well as vehicle operating control for the realization of the planning objectives. The structure of the work, correspondingly, comprises the ACV modeling, soil parameter estimation, energy consumption optimization, motion planning and operating control.
Firstly, based on a prototype ACV, the structure model and dynamic model of the targeted ACV are established. In the perspective of structure, the ACV consists of a lifting system and a propulsion system. The loss models of air pressure and flow are built for the former and the motion transfer model for the latter. In the perspective of dynamics, an 11 degree-of-freedom system is modeled by the Lagrangian method.
As an important aspect of identification for soft terrain environment, eight key soil parameters are estimated step-by-step. In succession, the hybrid g-EKF algorithm is employed for the calculation of the soil parameters related to the tractive effort, the logarithm-mean algorithm for those related to the vertical forces, and the least squares method for those related to the bulldozing resistances. Experiment results show that the estimation multi-solution problem can be eliminated and estimation accuracy can be improved.
Energy consumption optimization is studied next. By analyzing the relationships among the ACV’s running parameters (e.g. load distribution, slip ratio and velocity), energy consumption is simplified as a function with only respect to slip ratio and load distribution ratio. Then the designed Adaptive Bees Algorithm (ABA) is applied to optimize this function. Its results, as the steady-state objectives of the running parameters, will be used in the succeeding motion planning and operating control. Experiment results show that the ABA can improve both optimization accuracy and optimization efficiency with the help of the features of functional partitioning, parallel operation and adaptive patch size adjustment.
Motion planning is then made for the ACV by the Artificial Potential Field (APF) method, on the purpose of solving the instantaneous objectives of its linear velocity and angular velocity. For the APF modeling, multiple motion requirements are embodied in the contents of potential items, involving goal attractive potential, obstacle repulsive potential, dynamic safety potential and energy economy potential. In the APF application, the objectives of velocity direction and speed are determined respectively so as to improve calculation efficiency. Experiment results show that the potentials have different importance in applications and the major motion requirement could thus be satisfied.
Finally, operating control is implemented to the ACV. A parallel double-loop control system is designed to control its linear/angular velocities and load distribution ratio. In the control system, fuzzy-PID controllers make up its core whose fuzzy reasoning rules present the idea of parameter coordinated control. Relatively, velocity control tracks an instantaneous objective, so that dynamic performance is paid more attention to. On the contrary, load distribution radio control tracks a steady-state objective and thus attaches more importance to static performance. Experiment results show that the ACV could, under control, reach the destination with soft-landing. The variations of path and velocity are consistent with the theoretical analysis in this process. Moreover, the velocity control presents a good dynamic performance, as required, to benefit the vehicle’s safety, and the load distribution ratio control manifests a good static performance to decrease energy consumption.
Although a soft-terrain-used intelligent ACV is taken as the object of study here, the proposed techniques and research results could have broader applications. For example, the designed soil parameter estimation algorithm is universally reasonable for ACVs. The ABA, as a global optimization metaheuristic, is suitable for general functional optimization problems. Relying on the improvements to potential function composition, motion requirement coordination and motion planning implementation, the improved APF is worth taking up a place in the area of motion planning for intelligent vehicles and robots. Consequently, the above three respects constitute the innovations of the work.
本文将智能气垫车自主导航体系抽象为气垫车、真实环境和虚拟环境三大系统,将自主导航的工作流程归纳为基于传感器信息的真实环境识别和虚拟环境构建,基于虚拟环境的车辆运动规划,以及用以实现规划目标的车辆运行控制。因此,全文依次研究了气垫车建模、土壤参数估值、能耗优化、运动规划和运行控制。
首先,结合原理样车,建立了气垫车的结构模型和动力学模型。气垫车的主要结构包括垫升系统和驱动系统,针对垫升系统建立了其压强和流量的损失模型,针对驱动系统建立了其运动传递模型。此外,还利用拉格朗日法建立了11自由度车辆动力学模型。接下来,作为软地面环境识别的重要部分,对8个关键土壤参数进行了分步估值,依次利用g-EKF联合算法计算推力相关土壤参数,利用对数均值法计算垂向力相关土壤参数,利用最小二乘法计算推土阻力相关土壤参数。试验结果表明,分步估值算法能够消除估值多解问题,提高估值准确性。
继而进行了能耗优化研究。分析了气垫车运行参数(如载荷分配比、滑转率和车速等)之间的关系,将总能耗简化表示为载荷分配比和滑转率的函数,并设计了自适应蜜蜂算法进行参数寻优。优化的结果可作为运行参数的稳态目标值,用于后续运动规划和运行控制。试验结果表明,由于自适应蜜蜂算法具有功能划分、并行计算和局部搜索范围自适应调节的特点,它能够同时提高优化准确性和优化效率。
继而利用人工势场法对气垫车进行了运动规划,计算自主导航过程中其平动速度和转动速度的瞬态目标值。在建立势场时,多种运动要求以目标物吸引势、障碍物排斥势、动力学安全势和能耗经济势的形式予以表现。在运用势场时,对车速方向和速率进行分步规划,以提高计算效率。试验结果表明,在不同的工况下,各势函数项有不同的相对重要性,运动规划结果使当时主要的运动要求得以满足。
最后研究了气垫车的运行控制。采用以模糊PID控制器为核心的并行双环控制系统控制车速和载荷分配比,模糊推理规则的设计体现出参数协调控制的思想。相对而言,车速控制跟踪瞬态目标值,因而更加注重动态性能;载荷分配比控制跟踪稳态目标值,因而更加注重静态性能。仿真试验结果表明,在控制系统的作用下,气垫车能够顺利抵达目的地并实现软着陆,其行驶路径和速度的变化符合理论分析结果;轮速控制表现出良好的动态性能,有利于提高车辆的行驶安全性;载荷分配比控制表现出良好的静态性能,有利于降低车辆能耗。
本文虽以软地面智能气垫车作为研究对象,但其研究成果可向多个领域推广。例如,设计的土壤参数在线分步估值算法是一种针对气垫车辆的普适算法;自适应蜜蜂算法作为一种启发式全局优化算法,可用于一般的函数优化问题;改进的人工势场法在势函数构成、多种运动要求的协调以及运动规划的实施等方面均有所创新,可用于智能车辆和机器人的运动规划。上述三项技术也因此构成本文的创新点。
By combining the techniques in the fields of off-road vehicle and robotics, this work studies the autonomous navigation problem for intelligent air-cushion vehicles (ACVs) for soft terrain. ACVs are able to use the lifting system to adjust the pressure under the travelling mechanism and thus able to improve the crossing ability on soft terrain, such as swamp, beach and desert. By taking advantage of autonomous navigation and automatic control techniques, intelligent ACVs may substitute people in dangerous tasks like exploration, searching, cleaning and sampling. Consequently, this interdisciplinary study could have practical significance in many applications in industry, agriculture and military.
The autonomous navigation system of the targeted intelligent ACV is described with three sub-systems, namely, the ACV, a real environment, and a virtual environment. Its workflow is briefly summarized as the real environment identification and the virtual environment construction based on sensor information, vehicle motion planning implemented in the virtual environment, as well as vehicle operating control for the realization of the planning objectives. The structure of the work, correspondingly, comprises the ACV modeling, soil parameter estimation, energy consumption optimization, motion planning and operating control.
Firstly, based on a prototype ACV, the structure model and dynamic model of the targeted ACV are established. In the perspective of structure, the ACV consists of a lifting system and a propulsion system. The loss models of air pressure and flow are built for the former and the motion transfer model for the latter. In the perspective of dynamics, an 11 degree-of-freedom system is modeled by the Lagrangian method.
As an important aspect of identification for soft terrain environment, eight key soil parameters are estimated step-by-step. In succession, the hybrid g-EKF algorithm is employed for the calculation of the soil parameters related to the tractive effort, the logarithm-mean algorithm for those related to the vertical forces, and the least squares method for those related to the bulldozing resistances. Experiment results show that the estimation multi-solution problem can be eliminated and estimation accuracy can be improved.
Energy consumption optimization is studied next. By analyzing the relationships among the ACV’s running parameters (e.g. load distribution, slip ratio and velocity), energy consumption is simplified as a function with only respect to slip ratio and load distribution ratio. Then the designed Adaptive Bees Algorithm (ABA) is applied to optimize this function. Its results, as the steady-state objectives of the running parameters, will be used in the succeeding motion planning and operating control. Experiment results show that the ABA can improve both optimization accuracy and optimization efficiency with the help of the features of functional partitioning, parallel operation and adaptive patch size adjustment.
Motion planning is then made for the ACV by the Artificial Potential Field (APF) method, on the purpose of solving the instantaneous objectives of its linear velocity and angular velocity. For the APF modeling, multiple motion requirements are embodied in the contents of potential items, involving goal attractive potential, obstacle repulsive potential, dynamic safety potential and energy economy potential. In the APF application, the objectives of velocity direction and speed are determined respectively so as to improve calculation efficiency. Experiment results show that the potentials have different importance in applications and the major motion requirement could thus be satisfied.
Finally, operating control is implemented to the ACV. A parallel double-loop control system is designed to control its linear/angular velocities and load distribution ratio. In the control system, fuzzy-PID controllers make up its core whose fuzzy reasoning rules present the idea of parameter coordinated control. Relatively, velocity control tracks an instantaneous objective, so that dynamic performance is paid more attention to. On the contrary, load distribution radio control tracks a steady-state objective and thus attaches more importance to static performance. Experiment results show that the ACV could, under control, reach the destination with soft-landing. The variations of path and velocity are consistent with the theoretical analysis in this process. Moreover, the velocity control presents a good dynamic performance, as required, to benefit the vehicle’s safety, and the load distribution ratio control manifests a good static performance to decrease energy consumption.
Although a soft-terrain-used intelligent ACV is taken as the object of study here, the proposed techniques and research results could have broader applications. For example, the designed soil parameter estimation algorithm is universally reasonable for ACVs. The ABA, as a global optimization metaheuristic, is suitable for general functional optimization problems. Relying on the improvements to potential function composition, motion requirement coordination and motion planning implementation, the improved APF is worth taking up a place in the area of motion planning for intelligent vehicles and robots. Consequently, the above three respects constitute the innovations of the work.
引文
[1] Wong, J.Y. Theory of Ground Vehicles [M]. 3rd ed. New York: John Wiley & Sons, Inc. 2001.
[2]恽良.气垫船原理与设计[M].北京:国防工业出版社. 1990.
[3]张跃革.半履带式气垫车结构设计及节能机理的研究[D].长春:吉林工业大学. 2000.
[4]阿海.中国气垫船的发展历程[J].现代兵器. 1996(5): 6-8.
[5] Hovercraft. [25, August, 2010]. http://en.wikipedia.org/wiki/Hovercraft.
[6] The Hovercraft Museum. [25, August, 2010]. http://www.hovercraft-museum.org/index.html.
[7]气垫船. [25, August, 2010]. http://baike.baidu.com/view/22522.htm.
[8]第六机械工业部第七研究院第七○八研究所.国外气垫船图集[M]. 1979.
[9]第六机械工业部第七研究院第七○八研究所.国内气垫船图集[M]. 1979.
[10] Fantoni, I., Lozano, R., Mazenc, F., Pettersen, K.Y. Stabilization of a nonlinear underactuated hovercraft [C]. Proceedings of the 38th IEEE Conference on Decision and Control. Phoenix, USA, 1999: 2533-2538.
[11] Fantoni, I., Lozano, R., Mazenc, F., Pettersen, K.Y. Stabilization of a nonlinear underactuated hovercraft [J]. International Journal of Robust and Nonlinear Control. 2000, 10(8): 645-654.
[12] Matsuo, H., Matsuo, K. Nonlinear analysis of cushion stability of ACV’s in heave [J]. Transactions of the Japan Society for Aeronautical and Space Sciences. 1987, 29(86): 220-229.
[13] Hinchey, M.J., Sullivan, P.A. On hovercraft overwater heave stability [J]. Journal of Sound and Vibration. 1993, 163(2): 261-273.
[14] Osuka, K., Hayashi, R., Ono, T. Linear robust control of nonlinear systems (elevation control of hovercraft via multi-sectional control) [J]. JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing. 1995, 38(4): 701-707.
[15]左远源.侧壁式气垫船大倾角稳性的探讨[J].船舶工程. 1983(3): 10-19.
[16]马涛,周伟麟, Sullivan, P.A.围裙响应动力学及其对气垫船稳定性和适航性的影响[J].船舶工程. 1988(6): 40-45+26.
[17]吕世海,刘春光,马涛.气垫船高速纵稳性与艏裙流体动力性能分析[J].船舶. 2006(4): 6-11.
[18] Pozzi, A., Manzo, F., Luchini, P. Compressible flow in a hovercraft air cushion [J]. AIAA journal. 1993, 31(3): 528-533.
[19] Bruzzone, D., Sebastiani, L. Application of a panel method to the hydrodynamic analysis of advanced vehicles [Z], Marine, Offshore and Ice Technology. C.A. Brebbia, et al., Editors. Southampton: Computational Mechanics Publications. 1994: 21-29.
[20]张根泉.浅论高密度气垫船的空气舵设计及其气动力性能[J].船舶. 2003(6): 37-38.
[21]吕世海,马涛.两条大型全垫升气垫船的流体动力性能比较[J].船舶. 2005(1): 16-21.
[22]王绍明,姚征.全垫升气垫船内部流场的数值模拟与改进[J].船舶力学. 2007, 11(2): 179-184.
[23] Clayton, B.R., Tuckey, P.R. Recent hovercraft research in the United Kingdom– Part 2. Analytical work related to hovercraft dynamics [J]. High Speed Surface Craft. 1984, 23(2): 24-30.
[24] Clayton, B.R., Tuckey, P.R. Recent hovercraft research in the United Kingdom– Experimental work andfacilities related to hovercraft dynamics [J]. High-Speed Surface Craft. 1984, 23(1): 28-33.
[25] Craighead, I.A. Vibration and dynamics of off-road vehicles [C]. IMechE Conference on Vehicle Noise and Vibration. London, UK, 1984: 65-76.
[26] Moran, D.D. Pitch and roll hydrodynamics of a pericell hovercraft [J]. Canadian Aeronautics and Space Journal. 1986, 32(4): 314-320.
[27] Denery, T. Multi-domain modeling of the dynamics of a hovercraft for controller development [C]. 2005 AIAA Modeling and Simulation Technologies Conference and Exhibit. San Francisco, USA, 2005: 744-756.
[28]尤矢勤,恽良.气垫船的振动问题[J].舰船科学技术. 1990(6): 1-11.
[29]黄国梁,楼连根,刘天威.全垫升气垫船操纵运动仿真研究[J].海洋工程. 1997, 15(2): 16-23.
[30]周佳,唐文勇,张圣坤.全垫升气垫船耐波性参数敏感度分析[J].上海交通大学学报. 2009, 43(10): 1564-1567.
[31] Allison, J.L., Forstell, B.G., McCain, M.L. Fan design for hovercraft and impact of new technology [C]. International Conference on Fans. London, UK, 2004: 297-306.
[32] Lee, Y.T., Ahuja, V., Hosangadi, A., Slipper, M.E., Birkbeck, R., Mulvihill, L.P., Coleman, R.M. Investigation of an air supply centrifugal fan for air cushion vehicle: Impeller design and validation [C]. Proceedings of the ASME Turbo Expo. Berlin, Germany, 2008: 2273-2285.
[33] Shearer, B.F. Home front heroes: A biographical dictionary of Americans during wartime [M]. Santa Barbara, USA: Greenwood Publishing Group. 2007.
[34]吴文虎.变型设计应用于气垫船垫升风机的探讨[J].船舶. 1995(2): 37-39.
[35]吴文虎.气垫船垫升风扇[J].船舶. 2001(6): 34-38.
[36] Chung, J., Sullivan, P.A., Ma, T. Nonlinear heave dynamics of an air cushion vehicle bag-and-finger skirt [J]. Journal of Ship Research. 1999, 43(2): 79-94.
[37] Chung, J., Sullivan, P.A. Linear heave dynamics of an air-cushion vehicle bag-and-finger skirt [J]. Transactions of the Japan Society for Aeronautical and Space Sciences. 2000, 43(140): 39-45.
[38] Chung, J. Skirt-material damping effects on heave dynamics of an air-cushion-vehicle bag-and-finger skirt [J]. Canadian Aeronautics and Space Journal. 2002, 48(3): 201-212.
[39] Chung, J., Jung, T.C. Optimization of an air cushion vehicle bag and finger skirt using genetic algorithms [J]. Aerospace Science and Technology. 2004, 8(3): 219-229.
[40] Sullivan, P.A., Charest, P.A., Ma, T. Heave stiffness of an air cushion vehicle bag-and-finger skirt [J]. Journal of Ship Research. 1994, 38(4): 302-307.
[41] Sullivan, P.A., Chung, J. UTIAS research on the dynamics of the bag-and-finger air cushion vehicle skirt [J]. Canadian Aeronautics and Space Journal. 1999, 45(2): 194-205.
[42] Graham, T.A., Sullivan, P.A. Pitch-heave dynamics of a segmented skirt air cushion [J]. Journal of Ship Research. 2002, 46(2): 121-137.
[43]郑楠,邬廷芳,华怡.气垫船双囊裙底部变压区对成形影响的探讨[J].舰船科学技术. 1986(6): 27-34.
[44]周伟麟,马涛.气垫船响应围裙特性及设计参数分析方法[J].船舶. 1990(5): 27-33+51.
[45]何志飞.气垫船双囊裙设计的适定性研究[J].舰船科学技术. 1992(4): 8-12.
[46] Harrison, J.A. Investigation of the steady fluid flow within a simple plenum chamber [J]. High SpeedSurface Craft. 1982, 21(11): 32-34.
[47] Matsuo, H., Fujiwara, K., Mashima, K., Matsuo, K. Dynamics of fan-duct-plenum systems in large-amplitude oscillation [J]. Journal of Aircraft. 1989, 26(4): 302-307.
[48] Manikonda, V., Krishnaprasad, P.S. Controllability of Lie-Poisson reduced dynamics [C]. Proceedings of 1997 American Control Conference. Albuquerque, USA, 1997: 2203-2207.
[49] Manikonda, V., Krishnaprasad, P.S. Controllability of a class of underactuated mechanical systems with symmetry [J]. Automatica. 2002, 38(11): 1837-1850.
[50] Tyner, D.R., Lewis, A.D. Controllability of a hovercraft model (and two general results) [C]. Proceedings of the 43th IEEE Conference on Decision and Control. Nassau, Bahamas, 2004: 1204-1209.
[51] Tunstel, E., Hockemeier, S., Jamshidi, M. Fuzzy control of a hovercraft platform [J]. Engineering Applications of Artificial Intelligence. 1994, 7(5): 513-519.
[52] Tanaka, K., Iwasaki, M., Wang, H.O. Stable switching fuzzy control and its application to a hovercraft type vehicle [C]. Proceedings of the 9th IEEE International Conference on Fuzzy Systems. San Antonio, USA, 2000: 804-809.
[53] Sira-Ramírez, H. Dynamic second-order sliding mode control of the hovercraft vessel [J]. IEEE Transactions on Control Systems Technology. 2002, 10(6): 860-865.
[54] Defoort, M., Floquet, T., Kokosy, A., Perruquetti, W. Finite-time control of a class of MIMO nonlinear systems using high order integral sliding mode control [C]. Proceedings of the 9th International Workshop on Variable Structure Systems. Alghero, Italy, 2006: 133-138.
[55]王颖,吴翰声,马涛.基于对象模型的气垫船升沉自动控制专家系统[J].华东船舶工业学院学报(自然科学版). 1993, 7(3): 68-74.
[56]黄勇,边信黔,匡红波,付明玉.常规控制和模糊PID控制在全垫升气垫船航向控制中的应用[J].自动化技术与应用. 2005, 24(12): 36-38.
[57]王成龙,施小成,付明玉,边信黔.神经网络PID在全垫升气垫船航向控制中的应用[J].中国造船. 2008, 49(3): 62-67.
[58] LaValle, S.M., Kuffner Jr, J.J. Randomized kinodynamic planning [J]. International Journal of Robotics Research. 2001, 20(5): 378-400.
[59] Tanaka, K., Iwasaki, M., Wang, H.O. Switching control of an R/C hovercraft: Stabilization and smooth switching [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 2001, 31(6): 853-863.
[60] Hussain, M., Chughtai, M.A., Rehman, Z. Navigation test bed for remote control hovercraft [C]. Proceedings of the 11th IEEE International Multitopic Conference. Lahore, Pakistan, 2007: 1-6.
[61] Ariaei, F., Jonckheere, E. LDV approach to circular trajectory tracking of the underactuated hovercraft model [C]. Proceedings of 2006 American Control Conference. Minneapolis, USA, 2006: 3910-3915.
[62] Da?i?, D.B., Subbotin, M.V., Kokotovi?, P.V. Path-following approach to control effort reduction of tracking feedback laws [C]. Proceedings of the 44th IEEE Conference on Decision and Control and the European Control Conference. Sevilla, Spain, 2005: 7284-7289.
[63] Okawa, K., Yuta, S. Motion control for vehicle with unknown operating properties-on-line data acquisition and motion planning [C]. Proceedings of 2003 IEEE International Conference on Robotics and Automation. Taipei, China, 2003: 3409-3414.
[64]钟山.气垫船运动参数滤波方法与应用研究[D].哈尔滨:哈尔滨工程大学. 2005.
[65]张成亮.气垫船运动参数神经网络预测与IIR滤波研究[D].哈尔滨:哈尔滨工程大学. 2007.
[66] Bekker, M.G. Accomplishments and future tasks in off-road transportation [J]. Journal of Terramechanics. 1974, 11(2): 11-30.
[67] Bertelsen, W.R. Progress report on Bertelsen research and development of an air cushion crawler all-terrain vehicle [J]. Canadian Aeronautics and Space Journal. 1987, 33(2): 71-75.
[68] Abeels, P.F.J. Air cushion vehicles and soil erosion [J]. Journal of Terramechanics. 1976, 13(4): 201-210.
[69] O’Neill, E.B. Landing Vehicle Assault (LVA) [J]. SAE Technical Paper. 1977: art. no. 770340.
[70] Sloss, D.A., Brady, P.M. Evaluation of the Landing Vehicle Assault (LVA) over-land performance [J]. SAE Technical Paper. 1978(780127): 1-14.
[71] Azovtsev, A.I., Samsonov, S.V. Hydrodynamic characteristics of air-supported caterpillar tracks [J]. Fluid Dynamics. 1986, 21(2): 317-319.
[72] Riley, W.B. Dynamic and static characteristics of air cushion vehicles [J]. Naval Engineers Journal. 1995, 107(6): 93-94.
[73] Raheman, H. Feasibility of operating air cushion vehicle in wetland paddy field [J]. Journal of Terramechanics. 1990, 27(4): 321-330.
[74] Rahman, A., Mohiuddin, A.K.M., Ismail, A.F., Yahya, A., Hossain, A. Development of hybrid electrical air-cushion tracked vehicle for swamp peat [J]. Journal of Terramechanics. 2010, 47(1): 45-54.
[75] Otesteanu, M., Popa, D. Intelligent control for air cushion transporters [J]. WSEAS Transactions on Systems. 2005, 4(12): 2376-2383.
[76]刘大维,杨文志,李强,陈秉聪,陈德兴.气垫车辆的开发及进展现状[J].吉林大学学报(工学版). 1993, 23(4): 107-112.
[77]姚圣卓,张兰义,陈吉清,陈秉聪,张磊.推拉式气垫运载平台的结构设计及行走机理[J].吉林工业大学自然科学学报. 2001, 31(1): 1-5.
[78]宁素俭,陈吉清,罗哲.推拉式气垫平台行驶运动学[J].农业工程学报. 2000, 16(3): 12-14.
[79]张磊,姚圣卓,张兰义,陈吉清,陈秉聪.推拉式气垫运载平台单片机测控系统的设计[J].机电工程. 2000, 17(5): 23-25.
[80]张兰义,陈吉清,杨永海,姚圣卓,张磊.推拉式气垫运载平台液压系统的方案设计[J].农业机械学报. 2001, 32(4): 7-10.
[81]姚圣卓,张兰义,陈吉清,陈秉聪,张磊.推拉式气垫运载平台转向运动分析[J].农业机械学报. 2001, 32(3): 24-26+30.
[82]郝丽英,郭峰.基于PWM开关阀的气垫平台行驶位姿的最优控制研究[J].黑龙江工程学院学报. 2002, 16(1): 33-35.
[83]张兰义,桑兰芬,陈吉清,杨永海,姚圣卓.推拉式气垫运载平台推拉机构受力分析与计算[J].农业机械学报. 2003, 34(1): 1-4.
[84]战凯,陈秉聪,陈德兴,宁素俭.步行轮式气垫车的开发与应用[J].农业机械学报. 1990(3): 90-93.
[85]战凯,陈德兴,杨文志,宁素俭,张兰义.仿步行式气垫车辆转向时的运动性能和动力性能分析[J].有色金属. 1997, 49(1): 20-23+44.
[86]战凯,陈秉聪,陈德兴,宁素俭.步行轮式气垫车的稳定性、操纵性和行驶方向稳定性分析[J].吉林大学学报(工学版). 1991(2): 43-48.
[87]杨文志,战凯,王杰,陈德兴.步行轮式气垫车围裙-地面阻力特性分析[J].农业机械学报. 1993, 24(4): 6-10.
[88]李强,宁素俭,杨文志,陈秉聪.步行轮式气垫车重量匹配和功率匹配[J].吉林工业大学学报. 1995, 25(3): 1-7.
[89]李强,宁素俭,赵洪伟,陈秉聪.步行轮式气垫车系统力学模型的建立[J].农业机械学报. 1996, 27(3): 1-6.
[90]宁素俭,罗哲,刘大维,陈秉聪.步行轮式气垫车车轮及围裙转向阻力矩研究[J].农业工程学报. 1997(2): 120-124.
[91]刘英舜,杨永海,宁素俭,陈秉聪,管爱华.松软土壤上步行轮式气垫车功率优化的研究[J].农业工程学报. 1997(2): 125-129.
[92]宁素俭,杨文志,陈吉清,陈秉聪,宋福林.步行轮式气垫车平顺性研究[J].农业工程学报. 1997(2): 115-119.
[93]李强,宁素俭,罗哲.步行轮式气垫车的需求功率匹配[J].农业工程学报. 1998(1): 40-44.
[94]罗哲,陈静,张跃革,宁素俭,陈秉聪.软地面半履带气垫车设计原则与分析[J].农业机械学报. 1999, 30(4): 5-8.
[95]陈秉聪,王昕.半履带式气垫车最佳功率匹配的研究[J].青岛大学学报(工程技术版). 1999, 14(2): 1-3+8.
[96]张跃革,罗哲,陈静,陈秉聪,宁素俭.软地面半履带式气垫车的功率分析[J].农业机械学报. 2000, 31(1): 26-29.
[97]王昕.半履带式气垫车控制系统的研究[D].长春:吉林工业大学. 2000.
[98]王昕,罗哲,陈秉聪,宁素俭.半履带式气垫车的模糊控制系统[J].农业机械学报. 2000, 31(3): 24-26.
[99]罗哲,谢东.国家自然科学基金面上项目“软地面半履带气垫车载荷匹配及行驶姿态的控制研究”结题报告[R].上海交通大学. 2009.
[100]罗哲,喻凡,许烁,杜尚谦,李彬.半履带气垫车[P].中国,发明专利, CN101007531A. 2007.8.1.
[101]罗哲,喻凡,张洋,李彬,许烁.用于气垫混合车辆的弹性连接装置[P].中国,发明专利, CN1709731A. 2005.12.21.
[102]罗哲,喻凡,谢东.电动橡胶履带行走机构[P].中国,实用新型, CN201099289Y. 2008.8.13.
[103] Xu, S., Luo, Z., Yu, F. Study of fuel economy optimization for a semi-track air-cushion vehicle based on genetic algorithms and fuzzy control [C]. Proceedings of 2008 IEEE Conference on Vehicle Power and Propulsion (VPPC 2008). Harbin, China, 2008: 1-7.
[104] Xu, S., Yu, F., Luo, Z., Ji, Z., Pham, D.T., Qiu, R. Adaptive Bees Algorithm– Bioinspiration from honeybee foraging to optimize fuel economy of a semi-track air-cushion vehicle [C]. Proceedings of 2010 International Conference on Swarm Intelligence. Beijing, China, 2010.
[105] Xu, S., Yu, F., Luo, Z., Ji, Z., Pham, D.T., Qiu, R. Adaptive Bees Algorithm– Bioinspiration from honeybee foraging to optimize fuel economy of a semi-track air-cushion vehicle [J]. The Computer Journal. Online Publication: http://comjnl.oxfordjournals.org/content/early/2011/01/04/comjnl.bxq097?keytype=ref.
[106] Xie, D., Luo, Z., Yu, F. The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization [J]. Applied Mathematical Modelling. 2009, 33(6): 2831-2844.
[107]周科,罗哲,喻凡.基于遗传算法的半履带气垫车多参数优化[J].传动技术. 2008, 22(3): 15-18+48+31.
[108] Xie, D., Ma, C., Luo, Z., Yu, F. Pitch control for a semi-track air-cushion vehicle based on optimal power consumption [J]. SAE International Journal of Passenger Cars– Mechanical Systems. 2009, 2(1): 1136-1142.
[109]许烁,周科,罗哲,喻凡.半履带气垫车滑转率控制仿真[J].上海交通大学学报. 2008, 42(6): 982-985.
[110]许烁,罗哲,喻凡,周科,张勇超.基于总功耗最小的半履带气垫车滑转率控制仿真[J].系统仿真学报. 2008, 20(16): 4244-4247+4251.
[111]许烁,罗哲, Pham, D.T.,纪赜.气垫车辆土壤参数估值算法[J].上海交通大学学报. 2011, 45(4).
[112]何玮,刘昭度,姚圣卓,王斌.气垫车辆风机系统设计与计算[J].风机技术. 2005(3): 14-16+22.
[113]何玮,马岳峰,刘昭度,宋明.垫升车辆气垫流场分布仿真计算[J].液压气动与密封. 2005(2): 26-28.
[114]何玮,刘昭度,姚圣卓,吴利军.气垫车辆围裙结构设计与分析[J].机械工程学报. 2006, 42(S1): 235-238.
[115]何玮,刘昭度,马岳峰,宋明.气垫车辆静垫升稳定性分析[J].计算机仿真. 2006, 23(3): 204-206+229.
[116]唐国明.一种具有广阔前景的气垫船——气垫平台[J].江苏船舶. 1989(2): 5-11.
[117]郑燕斌.气垫车台主参数的设计方法[J].兰州理工大学学报. 1985, 11(3): 45-49.
[118]洪添胜,区颖刚,张泰岭,邵耀坚.步行船式车辆行走机构的运动分析[J].华南理工大学学报(自然科学版). 1996, 24(S1): 159-163.
[119] Nuske, S., Roberts, J., Wyeth, G. Robust outdoor visual localization using a three-dimensional-edge map [J]. Journal of Field Robotics. 2009, 26(9): 728-756.
[120] Fang, H., Yang, M., Yang, R., Wang, C. Ground-texture-based localization for intelligent vehicles [J]. IEEE Transactions on Intelligent Transportation Systems. 2009, 10(3): 463-468.
[121] Ye, C., Borenstein, J. A novel filter for terrain mapping with laser rangefinders [J]. IEEE Transactions on Robotics. 2004, 20(5): 913-921.
[122] Xu, S., Ji, Z., Pham, D.T., Soroka, A.J., Yu, F. Ultrasonic sensor bidirectional arc-carving mapping between grid-oriented related arcs [C]. Proceedings of the 5th International Virtual Conference on Intelligent Production Machines and Systems. Cardiff, UK, 2009: 347-353.
[123] Schleicher, D., Bergasa, L.M., Oca?a, M., Barea, R., López, M.E. Real-time hierarchical outdoor slam based on stereovision and GPS fusion [J]. IEEE Transactions on Intelligent Transportation Systems. 2009, 10(3): 440-452.
[124] Xu, S., Ji, Z., Pham, D.T., Yu, F. Simultaneous localization and mapping: Swarm robot mutual localization and sonar arc bidirectional carving mapping [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2011, 225(3): 733-744.
[125] Christensen, H.I., Hager, G.D. Sensing and estimation [Z], Springer Handbook of Robotics. B. Siciliano, Khatib, O., Editors. Berlin: Springer. 2008: 87-107.
[126] Bekker, M.G. Introduction to Terrain-Vehicle Systems [M]. Ann Arbor: University of Michigan Press. 1969.
[127]余志生.汽车理论(第3版) [M].北京:机械工业出版社. 2000.
[128] SwissRanger? SR4000 Overview. [25, August, 2010]. http://www.mesa-imaging.ch/prodview4k.php.
[129] Sonka, M., Hlavac, V., Boyle, R. Image Processing, Analysis, and Machine Vision [M]. 2nd ed. Pacific Grove: Brooks/Cole Publishing Company. 1998.
[130] Olson, C.F., Matthies, L.H., Schoppers, M., Maimone, M.W. Rover navigation using stereo ego-motion [J]. Robotics and Autonomous Systems. 2003, 43(4): 215-229.
[131] Wikipedia. Time-of-flight camera. [31, August, 2010]. http://en.wikipedia.org/wiki/Time-of-flight_camera.
[132] Castellanos, J.A., Neira, J., Tardos, J.D. Multisensor fusion for simultaneous localization and map building [J]. IEEE Transactions on Robotics and Automation. 2001, 17(6): 908-914.
[133] Panzieri, S., Pascucci, F., Ulivi, G. An outdoor navigation system using GPS and inertial platform [J]. IEEE/ASME Transactions on Mechatronics. 2002, 7(2): 134-142.
[134] Thrun, S., Burgard, W., Fox, D. Probabilistic Robotics [M]. Intelligent Robotics and Autonomous Agents. Cambridge: MIT Press. 2005.
[135] Hou, B., Han, J. Nonlinear estimation methods for autonomous tracked vehicle with slip [J]. Chinese Journal of Mechanical Engineering (English Edition). 2007, 20(4): 1-7.
[136]潘泉,杨峰,叶亮,梁彦,程咏梅.一类非线性滤波器——UKF综述[J].控制与决策. 2005, 20(5): 481-489+494
[137] Lin, H.H., Tsai, C.C., Hsu, J.C. Ultrasonic localization and pose tracking of an autonomous mobile robot via fuzzy adaptive extended information filtering [J]. IEEE Transactions on Instrumentation and Measurement. 2008, 57(9): 2024-2034.
[138] Thrun, S., Liu, Y., Koller, D., Ng, A.Y., Ghahramani, Z., Durrant-Whyte, H. Simultaneous localization and mapping with sparse extended information filters [J]. International Journal of Robotics Research. 2004, 23(7-8): 693-716.
[139] Kass, M., Solomon, J. Smoothed local histogram filters [J]. ACM Transactions on Graphics. 2010, 29(4): art. no. 100.
[140] Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., Nordlund, P.J. Particle filters for positioning, navigation, and tracking [J]. IEEE Transactions on Signal Processing. 2002, 50(2): 425-437.
[141] Grisetti, G., Stachniss, C., Burgard, W. Improved techniques for grid mapping with Rao-Blackwellized particle filters [J]. IEEE Transactions on Robotics. 2007, 23(1): 34-46.
[142] Howard, A., Seraji, H. Vision-based terrain characterization and traversability assessment [J]. Journal of Robotic Systems. 2001, 18(10): 577-587.
[143] Choset, H., Nagatani, K., Lazar, N.A. The Arc-transversal median algorithm: A geometric approach to increasing ultrasonic sensor azimuth accuracy [J]. IEEE Transactions on Robotics and Automation. 2003, 19(3): 513-522.
[144] Silver, D., Morales, D., Rekleitis, I., Lisien, B., Choset, H. Arc carving: Obtaining accurate, low latency maps from ultrasonic range sensors [C]. Proceedings of 2004 IEEE International Conference on Robotics and Automation. New Orleans, USA, 2004: 1554-1561.
[145] Kojima, A., Tamura, T., Fukunaga, K. Natural language description of human activities from video images based on concept hierarchy of actions [J]. International Journal of Computer Vision. 2002, 50(2): 171-184.
[146] Ryoo, M.S., Aggarwal, J.K. Semantic representation and recognition of continued and recursive human activities [J]. International Journal of Computer Vision. 2009, 82(1): 1-24.
[147] Siegwart, R., Nourbakhsh, I.R. Introduction to Autonomous Mobile Robots [M]. Cambridge: MIT Press. 2004.
[148] González-Ba?os, H.H., Hsu, D., Latombe, J.C. Motion planning: Recent developments [Z], Autonomous Mobile Robots: Sensing, Control, Decision-Making and Applications. S.S. Ge, Lewis, F.L., Editors. Boca Raton: CRC Press. 2006: 373-416.
[149] Sloot, P., Hoekstra, A., Tan, C., Dongarra, J., Overmars, M. Recent developments in motion planning [Z], The 10th International Conference on Conceptual Structures. Berlin: Springer. 2002: 3-13.
[150] Liu, C., Ang Jr, M.H., Krishnan, H., Yong, L.S. Virtual obstacle concept for local-minimum-recovery in potential-field based navigation [C]. Proceedings of 2000 IEEE International Conference on Robotics and Automation. San Francisco, USA, 2000: 983-988.
[151] Park, M.G., Lee, M.C. Real-time path planning in unknown environment and a virtual hill concept to escape local minima [C]. Proceedings of the 30th Annual Conference of the IEEE Industrial Electronics Society. Busan, South Korea, 2004: 2223-2228.
[152] Dijkstra, E.W. A note on two problems in connexion with graphs [J]. Numerische Mathematik. 1959, 1(1): 269-271.
[153] Hart, P.E., Nilsson, N.J., Raphael, B. A formal basis for the heuristic determination of minimum cost paths [J]. IEEE Transactions on Systems Science and Cybernetics. 1968, 4(2): 100-107.
[154]张毅,罗元,郑太雄.移动机器人技术及其应用[M].北京:电子工业出版社. 2007.
[155] Stentz, A. Optimal and efficient path planning for partially-known environments [C]. Proceedings of 1994 IEEE International Conference on Robotics and Automation. San. Diego, USA, 1994: 3310-3317.
[156] Stentz, A. The focussed D* algorithm for real-time replanning [C]. Proceedings of the 14th International Joint Conference on Artificial Intelligence. Montreal, Canada, 1995.
[157] Cai, Z., Wen, Z., Zou, X.B., Chen, B.F. A mobile robot path-planning approach under unknown environments [C]. Proceedings of the 17th IFAC World Congress. Coex, South Korea, 2008: 5389-5392.
[158] Lumelsky, V.J., Skewis, T. Incorporating range sensing in the robot navigation function [J]. IEEE Transactions on Systems, Man and Cybernetics. 1990, 20(5): 1058-1069.
[159] Lumelsky, V.J., Stepanov, A.A. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape [Z], Autonomous Robot Vehicles. I.J. Cox, Wilfong, G.T., Editors. New York: Springer. 1990: 363-390.
[160] Kamon, I., Rivlin, E. Sensory-based motion planning with global proofs [J]. IEEE Transactions on Robotics and Automation. 1997, 13(6): 814-822.
[161] Kamon, I., Rimon, E., Rivlin, E. TangentBug: A range-sensor-based navigation algorithm [J]. International Journal of Robotics Research. 1998, 17(9): 934-953.
[162] Langer, R.A., Coelho, L.S., Oliveira, G.H.C. K-Bug, a new bug approach for mobile robot’s path planning [C]. Proceedings of 2007 IEEE International Conference on Control Applications. Suntec City, Singapore, 2007: 403-408.
[163] Antich, J., Ortiz, A., Mínguez, J. ABUG: A fast Bug-derivative anytime path planner with provable suboptimality bounds [C]. Proceedings of the 14th International Conference on Advanced Robotics.Munich, Germany, 2009.
[164] Taylor, K., Lavalle, S.M. I-Bug: An intensity-based bug algorithm [C]. Proceedings of 2009 IEEE International Conference on Robotics and Automation. Kobe, Japan, 2009: 3981-3986.
[165] Borenstein, J., Koren, Y. The vector field histogram– Fast obstacle avoidance for mobile robots [J]. IEEE Transactions on Robotics and Automation. 1991, 7(3): 278-288.
[166] Ulrich, I., Borenstein, J. VFH+: Reliable obstacle avoidance for fast mobile robots [C]. Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium, 1998: 1572-1577.
[167] Ulrich, I., Borenstein, J. VFH*: Local obstacle avoidance with look-ahead verification [C]. Proceedings of 2000 IEEE International Conference on Robotics and Automation. San Francisco, USA, 2000: 2505-2511.
[168] An, D., Wang, H. VPH: A new laser radar based obstacle avoidance method for intelligent mobile robots [C]. Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, China, 2004: 4681-4685.
[169] Khatib, M., Jaouni, H., Chatila, R., Laumond, J.P. Dynamic path modification for car-like nonholonomic mobile robots [C]. Proceedings of 1997 IEEE International Conference on Robotics and Automation. Albuquerque, USA, 1997: 2920-2925.
[170] Schlegel, C. Fast local obstacle avoidance under kinematic and dynamic constraints for a mobile robot [C]. Proceedings of 1998 IEEE International Conference on Intelligent Robots and Systems. Victoria, Canada, 1998: 594-599.
[171] Simmons, R. Curvature-velocity method for local obstacle avoidance [C]. Proceedings of 1996 IEEE International Conference on Robotics and Automation. Minneapolis, USA, 1996: 3375-3382.
[172] Ko, N.Y., Simmons, R.G. Lane-Curvature Method for local obstacle avoidance [C]. Proceedings of 1998 IEEE International Conference on Intelligent Robots and Systems. Victoria, Canada, 1998: 1615-1621.
[173] Fernández, J.L., Sanz, R., Benayas, J.A., Diéguez, A.R. Improving collision avoidance for mobile robots in partially known environments: The beam curvature method [J]. Robotics and Autonomous Systems. 2004, 46(4): 205-219.
[174] Fox, D., Burgard, W., Thrun, S. The dynamic window approach to collision avoidance [J]. IEEE Robotics and Automation Magazine. 1997, 4(1): 23-33.
[175] Brock, O., Khatib, O. High-speed navigation using the global dynamic window approach [C]. Proceedings of 1999 IEEE International Conference on Robotics and Automation. Detroit, USA, 1999: 341-346.
[176] Fox, D., Burgard, W., Thrun, S., Cremers, A.B. Hybrid collision avoidance method for mobile robots [C]. Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium, 1998: 1238-1243.
[177] Minguez, J., Montano, L. Nearness diagram navigation (ND): A new real time collision avoidance approach [C]. Proceedings of 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems. Takamatsu, Japan, 2000: 2094-2100.
[178] Minguez, J., Montano, L., Simeon, T., Alami, R. Global Nearness Diagram Navigation (GND) [C]. Proceedings of 2001 IEEE International Conference on Robotics and Automation. Seoul, South Korea, 2001: 33-39.
[179] Minguez, J., Montano, L. Nearness Diagram (ND) navigation: Collision avoidance in troublesome scenarios [J]. IEEE Transactions on Robotics and Automation. 2004, 20(1): 45-59.
[180] Kavraki, L.E., LaValle, S.M. Motion planning [Z], Springer Handbook of Robotics. B. Siciliano, Khatib, O., Editors. Berlin: Springer. 2008: 109-131.
[181] Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots [C]. Proceedings of 1985 IEEE International Conference on Robotics and Automation. St. Louis, USA, 1985: 500-505.
[182] Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots [J]. International Journal of Robotics Research. 1986, 5(1): 90-98.
[183] Krogh, B.H. A generalized potential field approach to obstacle avoidance control [C]. Proceedings of 1984 International Robotics Research Conference. Bethlehem, USA, 1984: 1150-1156.
[184] Koren, Y., Borenstein, J. Potential field methods and their inherent limitations for mobile robot navigation [C]. Proceedings of 1991 IEEE International Conference on Robotics and Automation. Sacramento, USA, 1991: 1398-1404.
[185] Borenstein, J., Koren, Y. Real-time obstacle avoidance for fast mobile robots [J]. IEEE Transactions on Systems, Man and Cybernetics. 1989, 19(5): 1179-1187.
[186] Tilove, R.B. Local obstacle avoidance for mobile robots based on the method of artificial potentials [C]. Proceedings of 1990 IEEE International Conference on Robotics and Automation. Cincinnati, USA, 1990: 566-571.
[187] Borenstein, J., Koren, Y. The vector field histogram– Fast obstacle avoidance for mobile robots [J]. IEEE Transactions on Robotics and Automation. 1991, 7(3): 278-288.
[188] Kim, J.-O., Khosla, P.K. Real-time obstacle avoidance using harmonic potential functions [J]. IEEE Transactions on Robotics and Automation. 1992, 8(3): 338-349.
[189] Kim, J.-O., Khosla, P. Real-time obstacle avoidance using harmonic potential functions [C]. Proceedings of 1991 IEEE International Conference on Robotics and Automation. Sacramento, USA, 1991: 790-796.
[190] Guldner, J., Utkin, V.I. Sliding mode control for gradient tracking and robot navigation using artificial potential fields [J]. IEEE Transactions on Robotics and Automation. 1995, 11(2): 247-254.
[191] Haddad, H., Khatib, M., Lacroix, S., Chatila, R. Reactive navigation in outdoor environments using potential fields [C]. Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium, 1998: 1232-1237.
[192] Veelaert, P., Bogaerts, W. Ultrasonic potential field sensor for obstacle avoidance [J]. IEEE Transactions on Robotics and Automation. 1999, 15(4): 774-779.
[193] Park, M.G., Lee, M.C. Artificial potential field based path planning for mobile robots using a virtual obstacle concept [C]. Proceedings of 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Kobe, Japan, 2003: 735-740.
[194] Ge, S.S., Cui, Y.J. New potential functions for mobile robot path planning [J]. IEEE Transactions on Robotics and Automation. 2000, 16(5): 615-620.
[195] Montano, L., Asensio, J.R. Real-time robot navigation in unstructured environments using a 3D laser rangefinder [C]. Proceedings of 1997 IEEE International Conference on Intelligent Robots and Systems. Grenoble, France, 1997: 526-532.
[196] Cheng, G., Gu, J., Bai, T., Majdalawieh, O. A new efficient control algorithm using potential field: Extension to robot path tracking [C]. Canadian Conference on Electrical and Computer Engineering, 2004: 2035-2040.
[197] Shimoda, S., Kuroda, Y., Iagnemma, K. Potential field navigation of high speed unmanned ground vehicles on uneven terrain [C]. Proceedings of 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005: 2828-2833.
[198] Shimoda, S., Kuroda, Y., Iagnemma, K. High-speed navigation of unmanned ground vehicles on uneven terrain using potential fields [J]. Robotica. 2007, 25(4): 409-424.
[199] Spenko, M., Kuroda, Y., Dubowsky, S., Iagnemma, K. Hazard avoidance for high-speed mobile robots in rough terrain [J]. Journal of Field Robotics. 2006, 23(5): 311-331.
[200] Poty, A., Melchior, P., Oustaloup, A. Dynamic path planning by fractional potential [C]. Proceedings of the 2nd IEEE International Conference on Computational Cybernetics, 2004: 365-371.
[201] Ge, S.S., Cui, Y.J. Dynamic motion planning for mobile robots using potential field method [J]. Autonomous Robots. 2002, 13(3): 207-222.
[202] Mabrouk, M.H., McInnes, C.R. Solving the potential field local minimum problem using internal agent states [J]. Robotics and Autonomous Systems. 2008, 56(12): 1050-1060.
[203] Rimon, E., Koditschek, D.E. Exact robot navigation using artificial potential functions [J]. IEEE Transactions on Robotics and Automation. 1992, 8(5): 501-518.
[204] Singh, L., Stephanou, H., Wen, J. Real-time robot motion control with circulatory fields [C]. Proceedings of 1996 IEEE International Conference on Robotics and Automation. Minneapolis, USA, 1996: 2737-2742.
[205] Vanualailai, J., Sharma, B., Nakagiri, S.i. An asymptotically stable collision-avoidance system [J]. International Journal of Non-Linear Mechanics. 2008, 43(9): 925-932.
[206] Xue, Y., Lee, B.S., Han, D. Automatic collision avoidance of ships [J]. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. 2009, 223(1): 33-46.
[207] Yuan, X., Zhu, Q.D., Yan, Y.J. Collision avoidance planning in multi-robot system based on improved artificial potential field and rules [J]. Journal of Harbin Institute of Technology (New Series). 2009, 16(3): 413-418.
[208] Xie, L.J., Xie, G.R., Chen, H.W., Li, X.L. Solution to reinforcement learning problems with artificial potential field [J]. Journal of Central South University of Technology (English Edition). 2008, 15(4): 552-557.
[209] Caselli, S., Reggiani, M., Rocchi, R. Heuristic methods for randomized path planning in potential fields [C]. Proceedings of 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Baniff, Canada, 2001: 426-431.
[210] Zou, X.Y., Zhu, J. Virtual local target method for avoiding local minimum in potential field based robot navigation [J]. Journal of Zhejiang University: Science. 2003, 4(3): 264-269.
[211] Lewis, J.P., Weir, M.K. Subgoal chaining and the local minimum problem [C]. Proceedings of 1999 IEEE International Joint Conference on Neural Networks. Washington DC, USA, 1999: 1844-1849.
[212] Luh, G.C., Liu, W.W. Reactive immune network based mobile robot navigation [Z], Lecture Notes in Computer Science: Artificial Immune Systems. Berlin: Springer. 2004: 119-132.
[213] Luh, G.C., Liu, W.W. Motion planning for mobile robots in dynamic environments using a potential field immune network [J]. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 2007, 221(7): 1033-1046.
[214] Tanaka, Y., Tsuji, T., Kaneko, M., Morasso, P.G. Trajectory generation using time scaled Artificial PotentialField [C]. Proceedings of 1998 IEEE International Conference on Intelligent Robots and Systems. Victoria, Canada, 1998: 223-228.
[215] Vadakkepat, P., Tan, K.C., Wang, M.L. Evolutionary Artificial Potential Fields and their application in real time robot path planning [C]. Proceedings of 2000 IEEE Conference on Evolutionary Computation. La Jolla, USA, 2000: 256-263.
[216] Liu, J., Wu, J.-B. Evolutionary group robots for collective world modeling [C]. Proceedings of the 3rd Interantional Conference on Autonomous Agents. Seattle, USA, 1999: 48-55.
[217] Yuan, M., Wang, S.A., Wu, C., Li, K. APF-guided adaptive immune network algorithm for robot path planning [J]. Frontiers of Computer Science in China. 2009, 3(2): 247-255.
[218]袁明新,王孙安,李昆鹏,陈乃建.基于势场法优化的蚁群免疫网络路径规划研究[J].系统仿真学报. 2009, 21(15): 4686-4690.
[219] Shi, X.B., Wang, X., Bi, J., Liu, F., Yang, D., Liu, X.Y. A DR algorithm based on artificial potential field method [J]. Multimedia Tools and Applications. 2009, 45(1-3): 247-261.
[220] Shin, K.G., McKay, N.D. Minimum-time control of robotic manipulators with geometric path constraints [J]. IEEE Transactions on Automatic Control. 1985, AC-30(6): 531-541.
[221] Shin, K.G., McKay, N.D. A dynamic programming approach to trajectory planning of robotic manipulators [J]. IEEE Transactions on Automatic Control. 1986, AC-31(6): 491-500.
[222] Laumond, J.P., Sekhavat, S., Lamiraux, F. Guidelines in nonholonomic motion planning for mobile robots [Z], Robot Motion Planning and Control. J. Laumond, Editor. Berlin: Springer. 1998: 1-53.
[223] Ferbach, P. Method of progressive constraints for nonholonomic motion planning [C]. Proceedings of 1996 IEEE International Conference on Robotics and Automation. Minneapolis, USA, 1996: 2949-2955.
[224] Ferbach, P. A method of progressive constraints for nonholonomic motion planning [J]. IEEE Transactions on Robotics and Automation. 1998, 14(1): 172-179.
[225] ?gren, P., Svenmarck, P. A new control mode for teleoperated differential drive UGVs [C]. Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, USA, 2007: 5794-5799.
[226] Azizi, A.R.M., Hamiruce, M.M., Kamil, R.A.R. Dead reckoning of a skid steer mobile robot using fuzzy [J]. European Journal of Scientific Research. 2009, 30(2): 305-314.
[227] Talukder, A., Manduchi, R., Castano, R., Owens, K., Matthies, L., Castano, A., Hogg, R. Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles [C]. Proceedings of 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems. Lausanne, Switzerland, 2002: 708-713.
[228] Peng, J., Akella, S. Coordinating multiple robots with kinodynamic constraints along specified paths [J]. International Journal of Robotics Research. 2005, 24(4): 295-310.
[229] Bouhraoua, A., Merah, N., AlDajani, M., ElShafei, M. Design and implementation of an unmanned ground vehicle for security applications [C]. Proceedings of the 7th International Symposium on Mechatronics and its Applications. Sharjah, UAE, 2010.
[230] Chung, W., Fu, L.C., Hsu, S.H. Motion control [Z], Springer Handbook of Robotics. B. Siciliano, Khatib, O., Editors. Berlin: Springer. 2008: 133-159.
[231] Choi, Y., Chung, W.K. PID Trajectory Tracking Control for Mechanical Systems [M]. Lecture Notes in Control and Information Sciences, vol. 298. New York: Springer. 2004.
[232] Alvarez-Ramirez, J., Cervantes, I., Kelly, R. PID regulation of robot manipulators: Stability and performance [J]. Systems and Control Letters. 2000, 41(2): 73-83.
[233] Ziegler, J.B., Nichols, N.B. Optimum settings for automatic controllers [J]. ASME Transactions. 1942, 64: 759-768.
[234]王春民,刘兴明,嵇艳鞠.连续与离散控制系统[M].北京:科学出版社. 2008.
[235]刘金琨.先进PID控制MATLAB仿真(第2版) [M].北京:电子工业出版社. 2004.
[236] Xu, S., Zhou, K., Luo, Z., Yu, F. Study of fuzzy PID control for a semi-track air-cushion vehicle based on power consumption optimization [C]. Proceedings of 2006 IEEE International Conference on Vehicular Electronics and Safety. Shanghai, China, 2006: 440-444.
[237] Wang, Y.T., Chen, Y.C., Lin, M.C. Dynamic object tracking control for a non-holonomic wheeled autonomous robot [J]. Tamkang Journal of Science and Engineering. 2009, 12(3): 339-350.
[238] De Wit, C.C., Díaz, E.O., Perrier, M. Nonlinear control of an underwater vehicle/manipulator with composite dynamics [J]. IEEE Transactions on Control Systems Technology. 2000, 8(6): 948-960.
[239] Isidori, A. Nonlinear Control Systems [M]. 3rd ed. New York: Springer. 1995.
[240] Huang, Y., Cao, Q., Leng, C. The path-tracking controller based on dynamic model with slip for one four-wheeled OMR [J]. Industrial Robot. 2010, 37(2): 193-201.
[241]钱学森,宋健.工程控制论[M].北京:科学出版社. 1980.
[242] Yang, T., Liu, Z., Chen, H., Pei, R. Robust tracking control of mobile robot formation with obstacle avoidance [J]. Journal of Control Science and Engineering. 2007: art. no. 51841.
[243] Yoon, Y., Shin, J., Kim, H.J., Park, Y., Sastry, S. Model-predictive active steering and obstacle avoidance for autonomous ground vehicles [J]. Control Engineering Practice. 2009, 17(7): 741-750.
[244] Abdallah, C., Dawson, D.M., Dorato, P., Jamshidi, M. Survey of robust control for rigid robots [J]. IEEE Control Systems Magazine. 1991, 11(2): 24-30.
[245] Chen, B.S., Lee, C.H., Chang, Y.C. H∞tracking design of uncertain nonlinear SISO systems: Adaptive fuzzy approach [J]. IEEE Transactions on Fuzzy Systems. 1996, 4(1): 32-43.
[246] Park, J., Chung, W.K. Analytic nonlinear H∞inverse-optimal control for Euler-Lagrange system [J]. IEEE Transactions on Robotics and Automation. 2000, 16(6): 847-854.
[247] Ye, J. Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot [J]. Neurocomputing. 2008, 71(7-9): 1561-1565.
[248] Wang, H., Xie, Y. Prediction error based adaptive jacobian tracking of robots with uncertain kinematics and dynamics [J]. IEEE Transactions on Automatic Control. 2009, 54(12): 2889-2894.
[249] Espinosa, F., López, E., Mateos, R., Mazo, M., García, R. Advanced and intelligent control techniques applied to the drive control and path tracking systems on a robotic wheelchair [J]. Autonomous Robots. 2001, 11(2): 137-148.
[250] Narasimhan, M., Singh, S.N. Adaptive optimal control of an autonomous underwater vehicle in the dive plane using dorsal fins [J]. Ocean Engineering. 2006, 33(3-4): 404-416.
[251] Fierro, R., Lewis, F.L. Control of a nonholonomic mobile robot: Backsteppins kinematics into dynamics [J]. Journal of Robotic Systems. 1997, 14(3): 149-163.
[252]丛爽.神经网络、模糊系统及其在运动控制中的应用[M].合肥:中国科学技术大学出版社. 2001.
[253]褚蕾蕾,陈绥阳,周梦.计算智能的数学基础[M].北京:科学出版社. 2002.
[254]窦振中.模糊逻辑控制技术及其应用[M].北京:北京航空航天大学出版社. 1995.
[255] Qiu, R. Some Mathematical Aspects of Fuzzy Systems [D]. Cardiff: Cardiff University. 2004.
[256] Misir, D., Malki, H.A., Chen, G. Design and analysis of a fuzzy proportional-integral-derivative controller [J]. Fuzzy Sets and Systems. 1996, 79(3): 297-314.
[257] Carvajal, J., Chen, G., Ogmen, H. Fuzzy PID controller: Design, performance evaluation, and stability analysis [J]. Information sciences. 2000, 123(3): 249-270.
[258] Mann, G.K.I., Hu, B.G., Gosine, R.G. Analysis of direct action fuzzy PID controller structures [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 1999, 29(3): 371-388.
[259] Sun, Y.L., Er, M.J. Hybrid fuzzy control of robotics systems [J]. IEEE Transactions on Fuzzy Systems. 2004, 12(6): 755-765.
[260] Kodagoda, K.R.S., Wijesoma, W.S., Teoh, E.K. Fuzzy speed and steering control of an AGV [J]. IEEE Transactions on Control Systems Technology. 2002, 10(1): 112-120.
[261]蔡建羡,阮晓刚,甘家飞.两轮自平衡机器人系统建模与模糊自整定PID控制[J].北京工业大学学报. 2009, 35(12): 1603-1607.
[262]朱磊磊,陈军,白晓鸽,杨娜,苏清华.基于曲柄滑块机构原理导航的农业机器人设计[J].农业机械学报. 2009, 40(S1): 33-36.
[263] Wang, J.Z., Ji, J.T., Wang, H.R. Grey prediction fuzzy control of the target tracking system in a robot weapon [J]. Journal of Beijing Institute of Technology (English Edition). 2007, 16(4): 424-429.
[264] Puntunan, S., Parnichkun, M. Online self-tuning precompensation for a PID heading control of a flying robot [J]. International Journal of Advanced Robotic Systems. 2006, 3(4): 323-330.
[265] Chen, H.Y., Wang, J.Q., Zheng, P. Steering control of wheeled armored vehicle with brushless DC motor [J]. Journal of Beijing Institute of Technology (English Edition). 2005, 14(3): 310-313.
[266] Luo, Z., Yu, F., Chen, B.C. Design of a novel semi-tracked air-cushion vehicle for soft terrain [J]. International Journal of Vehicle Design. 2002, 31(1): 112-123.
[267]龙天渝,蔡增基.流体力学泵与风机[M].北京:中国建筑工业出版社. 1999.
[268]喻凡,林逸.汽车系统动力学[M].北京:机械工业出版社. 2005.
[269] Bekker, M.G. Theory of Land Locomotion [M]. Ann Arbor: University of Michigan Press. 1956.
[270] Ray, L.E. Estimation of terrain forces and parameters for rigid-wheeled vehicles [J]. IEEE Transactions on Robotics. 2009, 25(3): 717-726.
[271] Dumond, D.A., Ray, L., Trautmann, E. Evaluation of terrain parameter estimation using a stochastic terrain model [C]. Proceedings of SPIE, 2009: 73321F.
[272] Li, L., Sandu, C. On the impact of cargo weight, vehicle parameters, and terrain characteristics on the prediction of traction for off-road vehicles [J]. Journal of Terramechanics. 2007, 44(3): 221-238.
[273] Golda, D., Iagnemma, K., Dubowsky, S. Probabilistic modeling and analysis of high-speed rough-terrain mobile robots [C]. Proceedings of 2004 IEEE International Conference on Robotics and Automation. New Orleans, USA, 2004: 914-919.
[274] Hutangkabodee, S., Zweiri, Y.H., Seneviratne, L.D., Althoefer, K. Multi-solution problem for track-terrain interaction dynamics and lumped soil parameter identification [Z], Field and Service Robotics. Berlin: Springer. 2006: 517-528.
[275] Tarantola, A. Inverse Problem Theory and Methods for Model Parameter Estimation [M]. Philadelphia:SIAM. 2005.
[276] Le, A.T., Rye, D.C., Durrant-Whyte, H.F. Estimation of track-soil interactions for autonomous tracked vehicles [C]. Proceedings of 1997 IEEE International Conference on Robotics and Automation. Albuquerque, USA, 1997: 1388-1393.
[277] Iagnemma, K., Kang, S., Shibly, H., Dubowsky, S. Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers [J]. IEEE Transactions on Robotics. 2004, 20(5): 921-927.
[278] Hutangkabodee, S., Zweiri, Y.H., Seneviratne, L.D., Althoefer, K. Soil parameter identification for wheel-terrain interaction dynamics and traversability prediction [J]. International Journal of Automation and Computing. 2006, 3(3): 244-251.
[279] Luo, Z., Yu, F. Load distribution control system design for a semi-track air-cushion vehicle [J]. Journal of Terramechanics. 2007, 44(4): 319-325.
[280]许烁,罗哲,周科.基于功耗最优的半履带气垫车模糊PID控制仿真[J].上海交通大学学报. 2007, 41(6): 1026-1030.
[281]刘明树,程悦荪,王登峰,郑联珠,张友坤,王志中.软路面原始谱向有效谱的理论转化方法[J].农业机械学报. 1993, 24(4): 11-17.
[282]刘明树,郑联珠,张友坤.软路面有效不平度的理论研究[J].农业机械学报. 1999, 30(5): 1-4.
[283] Holland, J.E., Holland, C.W. Lightweight skirt assembly for air cushion vehicles [P]. USA patent, WO2005023616. 2004.12.8.
[284] von Frisch, K. The Dance Language and Orientation of Bees [M]. Cambridge, MA: Harvard University Press. 1967.
[285] Beekman, M., Lew, J.B. Foraging in honeybees– when does it pay to dance? [J]. Behavioral Ecology. 2008, 19(2): 255-261.
[286] Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M. The Bees Algorithm– A novel tool for complex optimisation problems [C]. Proceedings of the 2nd International Virtual Conference on Intelligent Production Machines and Systems. Cardiff, UK, 2006: 454-459.
[287] Ghanbarzadeh, A. The Bees Algorithm: A novel optimisation tool [D]. Cardiff. 2007.
[288] Pham, D.T., Ghanbarzadeh, A., Otri, S., Ko?, E. Optimal design of mechanical components using the Bees Algorithm [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2009, 223(5): 1051-1056.
[289] Pham, D.T., Afify, A.A., Koc, E. Manufacturing cell formation using the Bees Algorithm [C]. Proceedings of the 3rd International Virtual Conference on Intelligent Production Machines and Systems. Cardiff, UK, 2007: 523-528.
[290] Xu, S., Ji, Z., Pham, D.T., Yu, F. Bio-inspired binary bees algorithm for a two-level distribution optimisation problem [J]. Journal of Bionic Engineering. 2010, 7(2): 161-167.
[291] Xu, S., Ji, Z., Pham, D.T., Yu, F. Binary Bees Algorithm– Bioinspiration from the foraging mechanism of honeybees to optimize a multiobjective multidimensional assignment problem [J]. Engineering Optimization. Online Publication: http://www.informaworld.com/smpp/content~db=all?content=10.1080/0305215X.2010.542812&jumptype=alert&alerttype=ifirst_author_alert,email.
[292] Pham, D.T., Castellani, M. The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal ofMechanical Engineering Science. 2009, 223(12): 2919-2938.
[293] Ando, K., Miki, M., Hiroyasu, T. Multi-point simulated annealing with adaptive neighborhood [J]. IEICE Transactions on Information and Systems. 2007, E90-D(2): 457-464.
[294] Yang, Z., Tang, K., Yao, X. Self-adaptive differential evolution with neighborhood search [C]. Proceedings of 2008 IEEE Congress on Evolutionary Computation. Washington, D.C., USA, 2008: 1110-1116.
[295] Dhouib, S., Kharrat, A., Chabchoub, H. A variable neighbourhood search method to solve hierarchically engineering design problems [C]. Intelligent Systems and Automation: 2nd Mediterranean Conference on Intelligent Systems and Automation. Zarzis, Tunisia, 2009: 313-317.
[296] Fleszar, K., Osman, I.H., Hindi, K.S. A variable neighbourhood search algorithm for the open vehicle routing problem [J]. European Journal of Operational Research. 2009, 195(3): 803-809.
[297] Quilter, C.G. Foraging activity of the sand beach isopod Scyphax ornatus Dana [J]. New Zealand Journal of Zoology. 1987, 14(4): 433-439.
[298] Heinrich, B. Resource heterogeneity and patterns of movement in foraging bumblebees [J]. Oecologia. 1979, 40(3): 235-245.
[299] Burns, J.G., Dyer, A.G. Diversity of speed-accuracy strategies benefits social insects [J]. Current Biology. 2008, 18(20): R953-R954.
[300] Real, L.A. Uncertainty and pollinator-plant interactions: The foraging behavior of bees and wasps on artificial flowers [J]. Ecology. 1981, 62(1): 20-26.
[301] Rusu, R.B., Sundaresan, A., Morisset, B., Hauser, K., Agrawal, M., Jean-Claude, L., Beetz, M. Leaving Flatland: Effcient real-time three-dimensional perception and motion planning [J]. Journal of Field Robotics. 2009, 26(10): 841-862.
[302] Arbeiter, G., Fischer, J., Verl, A. 3D perception and modelling for manipulation on Care-O-bot 3 [C]. Proceedings of 2010 IEEE International Conference on Robotics and Automation. Anchorage, USA, 2010: in press.
[303] Global Positioning System. [10, November, 2010]. http://www.gps.gov/.
[304]宋小庆.履带车辆电驱动系统及数字控制研究[D].北京:北京理工大学. 2001.
[305]杨耕,罗应立.电机与运动控制系统[M].北京:清华大学出版社. 2006.
[2]恽良.气垫船原理与设计[M].北京:国防工业出版社. 1990.
[3]张跃革.半履带式气垫车结构设计及节能机理的研究[D].长春:吉林工业大学. 2000.
[4]阿海.中国气垫船的发展历程[J].现代兵器. 1996(5): 6-8.
[5] Hovercraft. [25, August, 2010]. http://en.wikipedia.org/wiki/Hovercraft.
[6] The Hovercraft Museum. [25, August, 2010]. http://www.hovercraft-museum.org/index.html.
[7]气垫船. [25, August, 2010]. http://baike.baidu.com/view/22522.htm.
[8]第六机械工业部第七研究院第七○八研究所.国外气垫船图集[M]. 1979.
[9]第六机械工业部第七研究院第七○八研究所.国内气垫船图集[M]. 1979.
[10] Fantoni, I., Lozano, R., Mazenc, F., Pettersen, K.Y. Stabilization of a nonlinear underactuated hovercraft [C]. Proceedings of the 38th IEEE Conference on Decision and Control. Phoenix, USA, 1999: 2533-2538.
[11] Fantoni, I., Lozano, R., Mazenc, F., Pettersen, K.Y. Stabilization of a nonlinear underactuated hovercraft [J]. International Journal of Robust and Nonlinear Control. 2000, 10(8): 645-654.
[12] Matsuo, H., Matsuo, K. Nonlinear analysis of cushion stability of ACV’s in heave [J]. Transactions of the Japan Society for Aeronautical and Space Sciences. 1987, 29(86): 220-229.
[13] Hinchey, M.J., Sullivan, P.A. On hovercraft overwater heave stability [J]. Journal of Sound and Vibration. 1993, 163(2): 261-273.
[14] Osuka, K., Hayashi, R., Ono, T. Linear robust control of nonlinear systems (elevation control of hovercraft via multi-sectional control) [J]. JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing. 1995, 38(4): 701-707.
[15]左远源.侧壁式气垫船大倾角稳性的探讨[J].船舶工程. 1983(3): 10-19.
[16]马涛,周伟麟, Sullivan, P.A.围裙响应动力学及其对气垫船稳定性和适航性的影响[J].船舶工程. 1988(6): 40-45+26.
[17]吕世海,刘春光,马涛.气垫船高速纵稳性与艏裙流体动力性能分析[J].船舶. 2006(4): 6-11.
[18] Pozzi, A., Manzo, F., Luchini, P. Compressible flow in a hovercraft air cushion [J]. AIAA journal. 1993, 31(3): 528-533.
[19] Bruzzone, D., Sebastiani, L. Application of a panel method to the hydrodynamic analysis of advanced vehicles [Z], Marine, Offshore and Ice Technology. C.A. Brebbia, et al., Editors. Southampton: Computational Mechanics Publications. 1994: 21-29.
[20]张根泉.浅论高密度气垫船的空气舵设计及其气动力性能[J].船舶. 2003(6): 37-38.
[21]吕世海,马涛.两条大型全垫升气垫船的流体动力性能比较[J].船舶. 2005(1): 16-21.
[22]王绍明,姚征.全垫升气垫船内部流场的数值模拟与改进[J].船舶力学. 2007, 11(2): 179-184.
[23] Clayton, B.R., Tuckey, P.R. Recent hovercraft research in the United Kingdom– Part 2. Analytical work related to hovercraft dynamics [J]. High Speed Surface Craft. 1984, 23(2): 24-30.
[24] Clayton, B.R., Tuckey, P.R. Recent hovercraft research in the United Kingdom– Experimental work andfacilities related to hovercraft dynamics [J]. High-Speed Surface Craft. 1984, 23(1): 28-33.
[25] Craighead, I.A. Vibration and dynamics of off-road vehicles [C]. IMechE Conference on Vehicle Noise and Vibration. London, UK, 1984: 65-76.
[26] Moran, D.D. Pitch and roll hydrodynamics of a pericell hovercraft [J]. Canadian Aeronautics and Space Journal. 1986, 32(4): 314-320.
[27] Denery, T. Multi-domain modeling of the dynamics of a hovercraft for controller development [C]. 2005 AIAA Modeling and Simulation Technologies Conference and Exhibit. San Francisco, USA, 2005: 744-756.
[28]尤矢勤,恽良.气垫船的振动问题[J].舰船科学技术. 1990(6): 1-11.
[29]黄国梁,楼连根,刘天威.全垫升气垫船操纵运动仿真研究[J].海洋工程. 1997, 15(2): 16-23.
[30]周佳,唐文勇,张圣坤.全垫升气垫船耐波性参数敏感度分析[J].上海交通大学学报. 2009, 43(10): 1564-1567.
[31] Allison, J.L., Forstell, B.G., McCain, M.L. Fan design for hovercraft and impact of new technology [C]. International Conference on Fans. London, UK, 2004: 297-306.
[32] Lee, Y.T., Ahuja, V., Hosangadi, A., Slipper, M.E., Birkbeck, R., Mulvihill, L.P., Coleman, R.M. Investigation of an air supply centrifugal fan for air cushion vehicle: Impeller design and validation [C]. Proceedings of the ASME Turbo Expo. Berlin, Germany, 2008: 2273-2285.
[33] Shearer, B.F. Home front heroes: A biographical dictionary of Americans during wartime [M]. Santa Barbara, USA: Greenwood Publishing Group. 2007.
[34]吴文虎.变型设计应用于气垫船垫升风机的探讨[J].船舶. 1995(2): 37-39.
[35]吴文虎.气垫船垫升风扇[J].船舶. 2001(6): 34-38.
[36] Chung, J., Sullivan, P.A., Ma, T. Nonlinear heave dynamics of an air cushion vehicle bag-and-finger skirt [J]. Journal of Ship Research. 1999, 43(2): 79-94.
[37] Chung, J., Sullivan, P.A. Linear heave dynamics of an air-cushion vehicle bag-and-finger skirt [J]. Transactions of the Japan Society for Aeronautical and Space Sciences. 2000, 43(140): 39-45.
[38] Chung, J. Skirt-material damping effects on heave dynamics of an air-cushion-vehicle bag-and-finger skirt [J]. Canadian Aeronautics and Space Journal. 2002, 48(3): 201-212.
[39] Chung, J., Jung, T.C. Optimization of an air cushion vehicle bag and finger skirt using genetic algorithms [J]. Aerospace Science and Technology. 2004, 8(3): 219-229.
[40] Sullivan, P.A., Charest, P.A., Ma, T. Heave stiffness of an air cushion vehicle bag-and-finger skirt [J]. Journal of Ship Research. 1994, 38(4): 302-307.
[41] Sullivan, P.A., Chung, J. UTIAS research on the dynamics of the bag-and-finger air cushion vehicle skirt [J]. Canadian Aeronautics and Space Journal. 1999, 45(2): 194-205.
[42] Graham, T.A., Sullivan, P.A. Pitch-heave dynamics of a segmented skirt air cushion [J]. Journal of Ship Research. 2002, 46(2): 121-137.
[43]郑楠,邬廷芳,华怡.气垫船双囊裙底部变压区对成形影响的探讨[J].舰船科学技术. 1986(6): 27-34.
[44]周伟麟,马涛.气垫船响应围裙特性及设计参数分析方法[J].船舶. 1990(5): 27-33+51.
[45]何志飞.气垫船双囊裙设计的适定性研究[J].舰船科学技术. 1992(4): 8-12.
[46] Harrison, J.A. Investigation of the steady fluid flow within a simple plenum chamber [J]. High SpeedSurface Craft. 1982, 21(11): 32-34.
[47] Matsuo, H., Fujiwara, K., Mashima, K., Matsuo, K. Dynamics of fan-duct-plenum systems in large-amplitude oscillation [J]. Journal of Aircraft. 1989, 26(4): 302-307.
[48] Manikonda, V., Krishnaprasad, P.S. Controllability of Lie-Poisson reduced dynamics [C]. Proceedings of 1997 American Control Conference. Albuquerque, USA, 1997: 2203-2207.
[49] Manikonda, V., Krishnaprasad, P.S. Controllability of a class of underactuated mechanical systems with symmetry [J]. Automatica. 2002, 38(11): 1837-1850.
[50] Tyner, D.R., Lewis, A.D. Controllability of a hovercraft model (and two general results) [C]. Proceedings of the 43th IEEE Conference on Decision and Control. Nassau, Bahamas, 2004: 1204-1209.
[51] Tunstel, E., Hockemeier, S., Jamshidi, M. Fuzzy control of a hovercraft platform [J]. Engineering Applications of Artificial Intelligence. 1994, 7(5): 513-519.
[52] Tanaka, K., Iwasaki, M., Wang, H.O. Stable switching fuzzy control and its application to a hovercraft type vehicle [C]. Proceedings of the 9th IEEE International Conference on Fuzzy Systems. San Antonio, USA, 2000: 804-809.
[53] Sira-Ramírez, H. Dynamic second-order sliding mode control of the hovercraft vessel [J]. IEEE Transactions on Control Systems Technology. 2002, 10(6): 860-865.
[54] Defoort, M., Floquet, T., Kokosy, A., Perruquetti, W. Finite-time control of a class of MIMO nonlinear systems using high order integral sliding mode control [C]. Proceedings of the 9th International Workshop on Variable Structure Systems. Alghero, Italy, 2006: 133-138.
[55]王颖,吴翰声,马涛.基于对象模型的气垫船升沉自动控制专家系统[J].华东船舶工业学院学报(自然科学版). 1993, 7(3): 68-74.
[56]黄勇,边信黔,匡红波,付明玉.常规控制和模糊PID控制在全垫升气垫船航向控制中的应用[J].自动化技术与应用. 2005, 24(12): 36-38.
[57]王成龙,施小成,付明玉,边信黔.神经网络PID在全垫升气垫船航向控制中的应用[J].中国造船. 2008, 49(3): 62-67.
[58] LaValle, S.M., Kuffner Jr, J.J. Randomized kinodynamic planning [J]. International Journal of Robotics Research. 2001, 20(5): 378-400.
[59] Tanaka, K., Iwasaki, M., Wang, H.O. Switching control of an R/C hovercraft: Stabilization and smooth switching [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 2001, 31(6): 853-863.
[60] Hussain, M., Chughtai, M.A., Rehman, Z. Navigation test bed for remote control hovercraft [C]. Proceedings of the 11th IEEE International Multitopic Conference. Lahore, Pakistan, 2007: 1-6.
[61] Ariaei, F., Jonckheere, E. LDV approach to circular trajectory tracking of the underactuated hovercraft model [C]. Proceedings of 2006 American Control Conference. Minneapolis, USA, 2006: 3910-3915.
[62] Da?i?, D.B., Subbotin, M.V., Kokotovi?, P.V. Path-following approach to control effort reduction of tracking feedback laws [C]. Proceedings of the 44th IEEE Conference on Decision and Control and the European Control Conference. Sevilla, Spain, 2005: 7284-7289.
[63] Okawa, K., Yuta, S. Motion control for vehicle with unknown operating properties-on-line data acquisition and motion planning [C]. Proceedings of 2003 IEEE International Conference on Robotics and Automation. Taipei, China, 2003: 3409-3414.
[64]钟山.气垫船运动参数滤波方法与应用研究[D].哈尔滨:哈尔滨工程大学. 2005.
[65]张成亮.气垫船运动参数神经网络预测与IIR滤波研究[D].哈尔滨:哈尔滨工程大学. 2007.
[66] Bekker, M.G. Accomplishments and future tasks in off-road transportation [J]. Journal of Terramechanics. 1974, 11(2): 11-30.
[67] Bertelsen, W.R. Progress report on Bertelsen research and development of an air cushion crawler all-terrain vehicle [J]. Canadian Aeronautics and Space Journal. 1987, 33(2): 71-75.
[68] Abeels, P.F.J. Air cushion vehicles and soil erosion [J]. Journal of Terramechanics. 1976, 13(4): 201-210.
[69] O’Neill, E.B. Landing Vehicle Assault (LVA) [J]. SAE Technical Paper. 1977: art. no. 770340.
[70] Sloss, D.A., Brady, P.M. Evaluation of the Landing Vehicle Assault (LVA) over-land performance [J]. SAE Technical Paper. 1978(780127): 1-14.
[71] Azovtsev, A.I., Samsonov, S.V. Hydrodynamic characteristics of air-supported caterpillar tracks [J]. Fluid Dynamics. 1986, 21(2): 317-319.
[72] Riley, W.B. Dynamic and static characteristics of air cushion vehicles [J]. Naval Engineers Journal. 1995, 107(6): 93-94.
[73] Raheman, H. Feasibility of operating air cushion vehicle in wetland paddy field [J]. Journal of Terramechanics. 1990, 27(4): 321-330.
[74] Rahman, A., Mohiuddin, A.K.M., Ismail, A.F., Yahya, A., Hossain, A. Development of hybrid electrical air-cushion tracked vehicle for swamp peat [J]. Journal of Terramechanics. 2010, 47(1): 45-54.
[75] Otesteanu, M., Popa, D. Intelligent control for air cushion transporters [J]. WSEAS Transactions on Systems. 2005, 4(12): 2376-2383.
[76]刘大维,杨文志,李强,陈秉聪,陈德兴.气垫车辆的开发及进展现状[J].吉林大学学报(工学版). 1993, 23(4): 107-112.
[77]姚圣卓,张兰义,陈吉清,陈秉聪,张磊.推拉式气垫运载平台的结构设计及行走机理[J].吉林工业大学自然科学学报. 2001, 31(1): 1-5.
[78]宁素俭,陈吉清,罗哲.推拉式气垫平台行驶运动学[J].农业工程学报. 2000, 16(3): 12-14.
[79]张磊,姚圣卓,张兰义,陈吉清,陈秉聪.推拉式气垫运载平台单片机测控系统的设计[J].机电工程. 2000, 17(5): 23-25.
[80]张兰义,陈吉清,杨永海,姚圣卓,张磊.推拉式气垫运载平台液压系统的方案设计[J].农业机械学报. 2001, 32(4): 7-10.
[81]姚圣卓,张兰义,陈吉清,陈秉聪,张磊.推拉式气垫运载平台转向运动分析[J].农业机械学报. 2001, 32(3): 24-26+30.
[82]郝丽英,郭峰.基于PWM开关阀的气垫平台行驶位姿的最优控制研究[J].黑龙江工程学院学报. 2002, 16(1): 33-35.
[83]张兰义,桑兰芬,陈吉清,杨永海,姚圣卓.推拉式气垫运载平台推拉机构受力分析与计算[J].农业机械学报. 2003, 34(1): 1-4.
[84]战凯,陈秉聪,陈德兴,宁素俭.步行轮式气垫车的开发与应用[J].农业机械学报. 1990(3): 90-93.
[85]战凯,陈德兴,杨文志,宁素俭,张兰义.仿步行式气垫车辆转向时的运动性能和动力性能分析[J].有色金属. 1997, 49(1): 20-23+44.
[86]战凯,陈秉聪,陈德兴,宁素俭.步行轮式气垫车的稳定性、操纵性和行驶方向稳定性分析[J].吉林大学学报(工学版). 1991(2): 43-48.
[87]杨文志,战凯,王杰,陈德兴.步行轮式气垫车围裙-地面阻力特性分析[J].农业机械学报. 1993, 24(4): 6-10.
[88]李强,宁素俭,杨文志,陈秉聪.步行轮式气垫车重量匹配和功率匹配[J].吉林工业大学学报. 1995, 25(3): 1-7.
[89]李强,宁素俭,赵洪伟,陈秉聪.步行轮式气垫车系统力学模型的建立[J].农业机械学报. 1996, 27(3): 1-6.
[90]宁素俭,罗哲,刘大维,陈秉聪.步行轮式气垫车车轮及围裙转向阻力矩研究[J].农业工程学报. 1997(2): 120-124.
[91]刘英舜,杨永海,宁素俭,陈秉聪,管爱华.松软土壤上步行轮式气垫车功率优化的研究[J].农业工程学报. 1997(2): 125-129.
[92]宁素俭,杨文志,陈吉清,陈秉聪,宋福林.步行轮式气垫车平顺性研究[J].农业工程学报. 1997(2): 115-119.
[93]李强,宁素俭,罗哲.步行轮式气垫车的需求功率匹配[J].农业工程学报. 1998(1): 40-44.
[94]罗哲,陈静,张跃革,宁素俭,陈秉聪.软地面半履带气垫车设计原则与分析[J].农业机械学报. 1999, 30(4): 5-8.
[95]陈秉聪,王昕.半履带式气垫车最佳功率匹配的研究[J].青岛大学学报(工程技术版). 1999, 14(2): 1-3+8.
[96]张跃革,罗哲,陈静,陈秉聪,宁素俭.软地面半履带式气垫车的功率分析[J].农业机械学报. 2000, 31(1): 26-29.
[97]王昕.半履带式气垫车控制系统的研究[D].长春:吉林工业大学. 2000.
[98]王昕,罗哲,陈秉聪,宁素俭.半履带式气垫车的模糊控制系统[J].农业机械学报. 2000, 31(3): 24-26.
[99]罗哲,谢东.国家自然科学基金面上项目“软地面半履带气垫车载荷匹配及行驶姿态的控制研究”结题报告[R].上海交通大学. 2009.
[100]罗哲,喻凡,许烁,杜尚谦,李彬.半履带气垫车[P].中国,发明专利, CN101007531A. 2007.8.1.
[101]罗哲,喻凡,张洋,李彬,许烁.用于气垫混合车辆的弹性连接装置[P].中国,发明专利, CN1709731A. 2005.12.21.
[102]罗哲,喻凡,谢东.电动橡胶履带行走机构[P].中国,实用新型, CN201099289Y. 2008.8.13.
[103] Xu, S., Luo, Z., Yu, F. Study of fuel economy optimization for a semi-track air-cushion vehicle based on genetic algorithms and fuzzy control [C]. Proceedings of 2008 IEEE Conference on Vehicle Power and Propulsion (VPPC 2008). Harbin, China, 2008: 1-7.
[104] Xu, S., Yu, F., Luo, Z., Ji, Z., Pham, D.T., Qiu, R. Adaptive Bees Algorithm– Bioinspiration from honeybee foraging to optimize fuel economy of a semi-track air-cushion vehicle [C]. Proceedings of 2010 International Conference on Swarm Intelligence. Beijing, China, 2010.
[105] Xu, S., Yu, F., Luo, Z., Ji, Z., Pham, D.T., Qiu, R. Adaptive Bees Algorithm– Bioinspiration from honeybee foraging to optimize fuel economy of a semi-track air-cushion vehicle [J]. The Computer Journal. Online Publication: http://comjnl.oxfordjournals.org/content/early/2011/01/04/comjnl.bxq097?keytype=ref.
[106] Xie, D., Luo, Z., Yu, F. The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization [J]. Applied Mathematical Modelling. 2009, 33(6): 2831-2844.
[107]周科,罗哲,喻凡.基于遗传算法的半履带气垫车多参数优化[J].传动技术. 2008, 22(3): 15-18+48+31.
[108] Xie, D., Ma, C., Luo, Z., Yu, F. Pitch control for a semi-track air-cushion vehicle based on optimal power consumption [J]. SAE International Journal of Passenger Cars– Mechanical Systems. 2009, 2(1): 1136-1142.
[109]许烁,周科,罗哲,喻凡.半履带气垫车滑转率控制仿真[J].上海交通大学学报. 2008, 42(6): 982-985.
[110]许烁,罗哲,喻凡,周科,张勇超.基于总功耗最小的半履带气垫车滑转率控制仿真[J].系统仿真学报. 2008, 20(16): 4244-4247+4251.
[111]许烁,罗哲, Pham, D.T.,纪赜.气垫车辆土壤参数估值算法[J].上海交通大学学报. 2011, 45(4).
[112]何玮,刘昭度,姚圣卓,王斌.气垫车辆风机系统设计与计算[J].风机技术. 2005(3): 14-16+22.
[113]何玮,马岳峰,刘昭度,宋明.垫升车辆气垫流场分布仿真计算[J].液压气动与密封. 2005(2): 26-28.
[114]何玮,刘昭度,姚圣卓,吴利军.气垫车辆围裙结构设计与分析[J].机械工程学报. 2006, 42(S1): 235-238.
[115]何玮,刘昭度,马岳峰,宋明.气垫车辆静垫升稳定性分析[J].计算机仿真. 2006, 23(3): 204-206+229.
[116]唐国明.一种具有广阔前景的气垫船——气垫平台[J].江苏船舶. 1989(2): 5-11.
[117]郑燕斌.气垫车台主参数的设计方法[J].兰州理工大学学报. 1985, 11(3): 45-49.
[118]洪添胜,区颖刚,张泰岭,邵耀坚.步行船式车辆行走机构的运动分析[J].华南理工大学学报(自然科学版). 1996, 24(S1): 159-163.
[119] Nuske, S., Roberts, J., Wyeth, G. Robust outdoor visual localization using a three-dimensional-edge map [J]. Journal of Field Robotics. 2009, 26(9): 728-756.
[120] Fang, H., Yang, M., Yang, R., Wang, C. Ground-texture-based localization for intelligent vehicles [J]. IEEE Transactions on Intelligent Transportation Systems. 2009, 10(3): 463-468.
[121] Ye, C., Borenstein, J. A novel filter for terrain mapping with laser rangefinders [J]. IEEE Transactions on Robotics. 2004, 20(5): 913-921.
[122] Xu, S., Ji, Z., Pham, D.T., Soroka, A.J., Yu, F. Ultrasonic sensor bidirectional arc-carving mapping between grid-oriented related arcs [C]. Proceedings of the 5th International Virtual Conference on Intelligent Production Machines and Systems. Cardiff, UK, 2009: 347-353.
[123] Schleicher, D., Bergasa, L.M., Oca?a, M., Barea, R., López, M.E. Real-time hierarchical outdoor slam based on stereovision and GPS fusion [J]. IEEE Transactions on Intelligent Transportation Systems. 2009, 10(3): 440-452.
[124] Xu, S., Ji, Z., Pham, D.T., Yu, F. Simultaneous localization and mapping: Swarm robot mutual localization and sonar arc bidirectional carving mapping [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2011, 225(3): 733-744.
[125] Christensen, H.I., Hager, G.D. Sensing and estimation [Z], Springer Handbook of Robotics. B. Siciliano, Khatib, O., Editors. Berlin: Springer. 2008: 87-107.
[126] Bekker, M.G. Introduction to Terrain-Vehicle Systems [M]. Ann Arbor: University of Michigan Press. 1969.
[127]余志生.汽车理论(第3版) [M].北京:机械工业出版社. 2000.
[128] SwissRanger? SR4000 Overview. [25, August, 2010]. http://www.mesa-imaging.ch/prodview4k.php.
[129] Sonka, M., Hlavac, V., Boyle, R. Image Processing, Analysis, and Machine Vision [M]. 2nd ed. Pacific Grove: Brooks/Cole Publishing Company. 1998.
[130] Olson, C.F., Matthies, L.H., Schoppers, M., Maimone, M.W. Rover navigation using stereo ego-motion [J]. Robotics and Autonomous Systems. 2003, 43(4): 215-229.
[131] Wikipedia. Time-of-flight camera. [31, August, 2010]. http://en.wikipedia.org/wiki/Time-of-flight_camera.
[132] Castellanos, J.A., Neira, J., Tardos, J.D. Multisensor fusion for simultaneous localization and map building [J]. IEEE Transactions on Robotics and Automation. 2001, 17(6): 908-914.
[133] Panzieri, S., Pascucci, F., Ulivi, G. An outdoor navigation system using GPS and inertial platform [J]. IEEE/ASME Transactions on Mechatronics. 2002, 7(2): 134-142.
[134] Thrun, S., Burgard, W., Fox, D. Probabilistic Robotics [M]. Intelligent Robotics and Autonomous Agents. Cambridge: MIT Press. 2005.
[135] Hou, B., Han, J. Nonlinear estimation methods for autonomous tracked vehicle with slip [J]. Chinese Journal of Mechanical Engineering (English Edition). 2007, 20(4): 1-7.
[136]潘泉,杨峰,叶亮,梁彦,程咏梅.一类非线性滤波器——UKF综述[J].控制与决策. 2005, 20(5): 481-489+494
[137] Lin, H.H., Tsai, C.C., Hsu, J.C. Ultrasonic localization and pose tracking of an autonomous mobile robot via fuzzy adaptive extended information filtering [J]. IEEE Transactions on Instrumentation and Measurement. 2008, 57(9): 2024-2034.
[138] Thrun, S., Liu, Y., Koller, D., Ng, A.Y., Ghahramani, Z., Durrant-Whyte, H. Simultaneous localization and mapping with sparse extended information filters [J]. International Journal of Robotics Research. 2004, 23(7-8): 693-716.
[139] Kass, M., Solomon, J. Smoothed local histogram filters [J]. ACM Transactions on Graphics. 2010, 29(4): art. no. 100.
[140] Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., Nordlund, P.J. Particle filters for positioning, navigation, and tracking [J]. IEEE Transactions on Signal Processing. 2002, 50(2): 425-437.
[141] Grisetti, G., Stachniss, C., Burgard, W. Improved techniques for grid mapping with Rao-Blackwellized particle filters [J]. IEEE Transactions on Robotics. 2007, 23(1): 34-46.
[142] Howard, A., Seraji, H. Vision-based terrain characterization and traversability assessment [J]. Journal of Robotic Systems. 2001, 18(10): 577-587.
[143] Choset, H., Nagatani, K., Lazar, N.A. The Arc-transversal median algorithm: A geometric approach to increasing ultrasonic sensor azimuth accuracy [J]. IEEE Transactions on Robotics and Automation. 2003, 19(3): 513-522.
[144] Silver, D., Morales, D., Rekleitis, I., Lisien, B., Choset, H. Arc carving: Obtaining accurate, low latency maps from ultrasonic range sensors [C]. Proceedings of 2004 IEEE International Conference on Robotics and Automation. New Orleans, USA, 2004: 1554-1561.
[145] Kojima, A., Tamura, T., Fukunaga, K. Natural language description of human activities from video images based on concept hierarchy of actions [J]. International Journal of Computer Vision. 2002, 50(2): 171-184.
[146] Ryoo, M.S., Aggarwal, J.K. Semantic representation and recognition of continued and recursive human activities [J]. International Journal of Computer Vision. 2009, 82(1): 1-24.
[147] Siegwart, R., Nourbakhsh, I.R. Introduction to Autonomous Mobile Robots [M]. Cambridge: MIT Press. 2004.
[148] González-Ba?os, H.H., Hsu, D., Latombe, J.C. Motion planning: Recent developments [Z], Autonomous Mobile Robots: Sensing, Control, Decision-Making and Applications. S.S. Ge, Lewis, F.L., Editors. Boca Raton: CRC Press. 2006: 373-416.
[149] Sloot, P., Hoekstra, A., Tan, C., Dongarra, J., Overmars, M. Recent developments in motion planning [Z], The 10th International Conference on Conceptual Structures. Berlin: Springer. 2002: 3-13.
[150] Liu, C., Ang Jr, M.H., Krishnan, H., Yong, L.S. Virtual obstacle concept for local-minimum-recovery in potential-field based navigation [C]. Proceedings of 2000 IEEE International Conference on Robotics and Automation. San Francisco, USA, 2000: 983-988.
[151] Park, M.G., Lee, M.C. Real-time path planning in unknown environment and a virtual hill concept to escape local minima [C]. Proceedings of the 30th Annual Conference of the IEEE Industrial Electronics Society. Busan, South Korea, 2004: 2223-2228.
[152] Dijkstra, E.W. A note on two problems in connexion with graphs [J]. Numerische Mathematik. 1959, 1(1): 269-271.
[153] Hart, P.E., Nilsson, N.J., Raphael, B. A formal basis for the heuristic determination of minimum cost paths [J]. IEEE Transactions on Systems Science and Cybernetics. 1968, 4(2): 100-107.
[154]张毅,罗元,郑太雄.移动机器人技术及其应用[M].北京:电子工业出版社. 2007.
[155] Stentz, A. Optimal and efficient path planning for partially-known environments [C]. Proceedings of 1994 IEEE International Conference on Robotics and Automation. San. Diego, USA, 1994: 3310-3317.
[156] Stentz, A. The focussed D* algorithm for real-time replanning [C]. Proceedings of the 14th International Joint Conference on Artificial Intelligence. Montreal, Canada, 1995.
[157] Cai, Z., Wen, Z., Zou, X.B., Chen, B.F. A mobile robot path-planning approach under unknown environments [C]. Proceedings of the 17th IFAC World Congress. Coex, South Korea, 2008: 5389-5392.
[158] Lumelsky, V.J., Skewis, T. Incorporating range sensing in the robot navigation function [J]. IEEE Transactions on Systems, Man and Cybernetics. 1990, 20(5): 1058-1069.
[159] Lumelsky, V.J., Stepanov, A.A. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape [Z], Autonomous Robot Vehicles. I.J. Cox, Wilfong, G.T., Editors. New York: Springer. 1990: 363-390.
[160] Kamon, I., Rivlin, E. Sensory-based motion planning with global proofs [J]. IEEE Transactions on Robotics and Automation. 1997, 13(6): 814-822.
[161] Kamon, I., Rimon, E., Rivlin, E. TangentBug: A range-sensor-based navigation algorithm [J]. International Journal of Robotics Research. 1998, 17(9): 934-953.
[162] Langer, R.A., Coelho, L.S., Oliveira, G.H.C. K-Bug, a new bug approach for mobile robot’s path planning [C]. Proceedings of 2007 IEEE International Conference on Control Applications. Suntec City, Singapore, 2007: 403-408.
[163] Antich, J., Ortiz, A., Mínguez, J. ABUG: A fast Bug-derivative anytime path planner with provable suboptimality bounds [C]. Proceedings of the 14th International Conference on Advanced Robotics.Munich, Germany, 2009.
[164] Taylor, K., Lavalle, S.M. I-Bug: An intensity-based bug algorithm [C]. Proceedings of 2009 IEEE International Conference on Robotics and Automation. Kobe, Japan, 2009: 3981-3986.
[165] Borenstein, J., Koren, Y. The vector field histogram– Fast obstacle avoidance for mobile robots [J]. IEEE Transactions on Robotics and Automation. 1991, 7(3): 278-288.
[166] Ulrich, I., Borenstein, J. VFH+: Reliable obstacle avoidance for fast mobile robots [C]. Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium, 1998: 1572-1577.
[167] Ulrich, I., Borenstein, J. VFH*: Local obstacle avoidance with look-ahead verification [C]. Proceedings of 2000 IEEE International Conference on Robotics and Automation. San Francisco, USA, 2000: 2505-2511.
[168] An, D., Wang, H. VPH: A new laser radar based obstacle avoidance method for intelligent mobile robots [C]. Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, China, 2004: 4681-4685.
[169] Khatib, M., Jaouni, H., Chatila, R., Laumond, J.P. Dynamic path modification for car-like nonholonomic mobile robots [C]. Proceedings of 1997 IEEE International Conference on Robotics and Automation. Albuquerque, USA, 1997: 2920-2925.
[170] Schlegel, C. Fast local obstacle avoidance under kinematic and dynamic constraints for a mobile robot [C]. Proceedings of 1998 IEEE International Conference on Intelligent Robots and Systems. Victoria, Canada, 1998: 594-599.
[171] Simmons, R. Curvature-velocity method for local obstacle avoidance [C]. Proceedings of 1996 IEEE International Conference on Robotics and Automation. Minneapolis, USA, 1996: 3375-3382.
[172] Ko, N.Y., Simmons, R.G. Lane-Curvature Method for local obstacle avoidance [C]. Proceedings of 1998 IEEE International Conference on Intelligent Robots and Systems. Victoria, Canada, 1998: 1615-1621.
[173] Fernández, J.L., Sanz, R., Benayas, J.A., Diéguez, A.R. Improving collision avoidance for mobile robots in partially known environments: The beam curvature method [J]. Robotics and Autonomous Systems. 2004, 46(4): 205-219.
[174] Fox, D., Burgard, W., Thrun, S. The dynamic window approach to collision avoidance [J]. IEEE Robotics and Automation Magazine. 1997, 4(1): 23-33.
[175] Brock, O., Khatib, O. High-speed navigation using the global dynamic window approach [C]. Proceedings of 1999 IEEE International Conference on Robotics and Automation. Detroit, USA, 1999: 341-346.
[176] Fox, D., Burgard, W., Thrun, S., Cremers, A.B. Hybrid collision avoidance method for mobile robots [C]. Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium, 1998: 1238-1243.
[177] Minguez, J., Montano, L. Nearness diagram navigation (ND): A new real time collision avoidance approach [C]. Proceedings of 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems. Takamatsu, Japan, 2000: 2094-2100.
[178] Minguez, J., Montano, L., Simeon, T., Alami, R. Global Nearness Diagram Navigation (GND) [C]. Proceedings of 2001 IEEE International Conference on Robotics and Automation. Seoul, South Korea, 2001: 33-39.
[179] Minguez, J., Montano, L. Nearness Diagram (ND) navigation: Collision avoidance in troublesome scenarios [J]. IEEE Transactions on Robotics and Automation. 2004, 20(1): 45-59.
[180] Kavraki, L.E., LaValle, S.M. Motion planning [Z], Springer Handbook of Robotics. B. Siciliano, Khatib, O., Editors. Berlin: Springer. 2008: 109-131.
[181] Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots [C]. Proceedings of 1985 IEEE International Conference on Robotics and Automation. St. Louis, USA, 1985: 500-505.
[182] Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots [J]. International Journal of Robotics Research. 1986, 5(1): 90-98.
[183] Krogh, B.H. A generalized potential field approach to obstacle avoidance control [C]. Proceedings of 1984 International Robotics Research Conference. Bethlehem, USA, 1984: 1150-1156.
[184] Koren, Y., Borenstein, J. Potential field methods and their inherent limitations for mobile robot navigation [C]. Proceedings of 1991 IEEE International Conference on Robotics and Automation. Sacramento, USA, 1991: 1398-1404.
[185] Borenstein, J., Koren, Y. Real-time obstacle avoidance for fast mobile robots [J]. IEEE Transactions on Systems, Man and Cybernetics. 1989, 19(5): 1179-1187.
[186] Tilove, R.B. Local obstacle avoidance for mobile robots based on the method of artificial potentials [C]. Proceedings of 1990 IEEE International Conference on Robotics and Automation. Cincinnati, USA, 1990: 566-571.
[187] Borenstein, J., Koren, Y. The vector field histogram– Fast obstacle avoidance for mobile robots [J]. IEEE Transactions on Robotics and Automation. 1991, 7(3): 278-288.
[188] Kim, J.-O., Khosla, P.K. Real-time obstacle avoidance using harmonic potential functions [J]. IEEE Transactions on Robotics and Automation. 1992, 8(3): 338-349.
[189] Kim, J.-O., Khosla, P. Real-time obstacle avoidance using harmonic potential functions [C]. Proceedings of 1991 IEEE International Conference on Robotics and Automation. Sacramento, USA, 1991: 790-796.
[190] Guldner, J., Utkin, V.I. Sliding mode control for gradient tracking and robot navigation using artificial potential fields [J]. IEEE Transactions on Robotics and Automation. 1995, 11(2): 247-254.
[191] Haddad, H., Khatib, M., Lacroix, S., Chatila, R. Reactive navigation in outdoor environments using potential fields [C]. Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium, 1998: 1232-1237.
[192] Veelaert, P., Bogaerts, W. Ultrasonic potential field sensor for obstacle avoidance [J]. IEEE Transactions on Robotics and Automation. 1999, 15(4): 774-779.
[193] Park, M.G., Lee, M.C. Artificial potential field based path planning for mobile robots using a virtual obstacle concept [C]. Proceedings of 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Kobe, Japan, 2003: 735-740.
[194] Ge, S.S., Cui, Y.J. New potential functions for mobile robot path planning [J]. IEEE Transactions on Robotics and Automation. 2000, 16(5): 615-620.
[195] Montano, L., Asensio, J.R. Real-time robot navigation in unstructured environments using a 3D laser rangefinder [C]. Proceedings of 1997 IEEE International Conference on Intelligent Robots and Systems. Grenoble, France, 1997: 526-532.
[196] Cheng, G., Gu, J., Bai, T., Majdalawieh, O. A new efficient control algorithm using potential field: Extension to robot path tracking [C]. Canadian Conference on Electrical and Computer Engineering, 2004: 2035-2040.
[197] Shimoda, S., Kuroda, Y., Iagnemma, K. Potential field navigation of high speed unmanned ground vehicles on uneven terrain [C]. Proceedings of 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005: 2828-2833.
[198] Shimoda, S., Kuroda, Y., Iagnemma, K. High-speed navigation of unmanned ground vehicles on uneven terrain using potential fields [J]. Robotica. 2007, 25(4): 409-424.
[199] Spenko, M., Kuroda, Y., Dubowsky, S., Iagnemma, K. Hazard avoidance for high-speed mobile robots in rough terrain [J]. Journal of Field Robotics. 2006, 23(5): 311-331.
[200] Poty, A., Melchior, P., Oustaloup, A. Dynamic path planning by fractional potential [C]. Proceedings of the 2nd IEEE International Conference on Computational Cybernetics, 2004: 365-371.
[201] Ge, S.S., Cui, Y.J. Dynamic motion planning for mobile robots using potential field method [J]. Autonomous Robots. 2002, 13(3): 207-222.
[202] Mabrouk, M.H., McInnes, C.R. Solving the potential field local minimum problem using internal agent states [J]. Robotics and Autonomous Systems. 2008, 56(12): 1050-1060.
[203] Rimon, E., Koditschek, D.E. Exact robot navigation using artificial potential functions [J]. IEEE Transactions on Robotics and Automation. 1992, 8(5): 501-518.
[204] Singh, L., Stephanou, H., Wen, J. Real-time robot motion control with circulatory fields [C]. Proceedings of 1996 IEEE International Conference on Robotics and Automation. Minneapolis, USA, 1996: 2737-2742.
[205] Vanualailai, J., Sharma, B., Nakagiri, S.i. An asymptotically stable collision-avoidance system [J]. International Journal of Non-Linear Mechanics. 2008, 43(9): 925-932.
[206] Xue, Y., Lee, B.S., Han, D. Automatic collision avoidance of ships [J]. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. 2009, 223(1): 33-46.
[207] Yuan, X., Zhu, Q.D., Yan, Y.J. Collision avoidance planning in multi-robot system based on improved artificial potential field and rules [J]. Journal of Harbin Institute of Technology (New Series). 2009, 16(3): 413-418.
[208] Xie, L.J., Xie, G.R., Chen, H.W., Li, X.L. Solution to reinforcement learning problems with artificial potential field [J]. Journal of Central South University of Technology (English Edition). 2008, 15(4): 552-557.
[209] Caselli, S., Reggiani, M., Rocchi, R. Heuristic methods for randomized path planning in potential fields [C]. Proceedings of 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Baniff, Canada, 2001: 426-431.
[210] Zou, X.Y., Zhu, J. Virtual local target method for avoiding local minimum in potential field based robot navigation [J]. Journal of Zhejiang University: Science. 2003, 4(3): 264-269.
[211] Lewis, J.P., Weir, M.K. Subgoal chaining and the local minimum problem [C]. Proceedings of 1999 IEEE International Joint Conference on Neural Networks. Washington DC, USA, 1999: 1844-1849.
[212] Luh, G.C., Liu, W.W. Reactive immune network based mobile robot navigation [Z], Lecture Notes in Computer Science: Artificial Immune Systems. Berlin: Springer. 2004: 119-132.
[213] Luh, G.C., Liu, W.W. Motion planning for mobile robots in dynamic environments using a potential field immune network [J]. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 2007, 221(7): 1033-1046.
[214] Tanaka, Y., Tsuji, T., Kaneko, M., Morasso, P.G. Trajectory generation using time scaled Artificial PotentialField [C]. Proceedings of 1998 IEEE International Conference on Intelligent Robots and Systems. Victoria, Canada, 1998: 223-228.
[215] Vadakkepat, P., Tan, K.C., Wang, M.L. Evolutionary Artificial Potential Fields and their application in real time robot path planning [C]. Proceedings of 2000 IEEE Conference on Evolutionary Computation. La Jolla, USA, 2000: 256-263.
[216] Liu, J., Wu, J.-B. Evolutionary group robots for collective world modeling [C]. Proceedings of the 3rd Interantional Conference on Autonomous Agents. Seattle, USA, 1999: 48-55.
[217] Yuan, M., Wang, S.A., Wu, C., Li, K. APF-guided adaptive immune network algorithm for robot path planning [J]. Frontiers of Computer Science in China. 2009, 3(2): 247-255.
[218]袁明新,王孙安,李昆鹏,陈乃建.基于势场法优化的蚁群免疫网络路径规划研究[J].系统仿真学报. 2009, 21(15): 4686-4690.
[219] Shi, X.B., Wang, X., Bi, J., Liu, F., Yang, D., Liu, X.Y. A DR algorithm based on artificial potential field method [J]. Multimedia Tools and Applications. 2009, 45(1-3): 247-261.
[220] Shin, K.G., McKay, N.D. Minimum-time control of robotic manipulators with geometric path constraints [J]. IEEE Transactions on Automatic Control. 1985, AC-30(6): 531-541.
[221] Shin, K.G., McKay, N.D. A dynamic programming approach to trajectory planning of robotic manipulators [J]. IEEE Transactions on Automatic Control. 1986, AC-31(6): 491-500.
[222] Laumond, J.P., Sekhavat, S., Lamiraux, F. Guidelines in nonholonomic motion planning for mobile robots [Z], Robot Motion Planning and Control. J. Laumond, Editor. Berlin: Springer. 1998: 1-53.
[223] Ferbach, P. Method of progressive constraints for nonholonomic motion planning [C]. Proceedings of 1996 IEEE International Conference on Robotics and Automation. Minneapolis, USA, 1996: 2949-2955.
[224] Ferbach, P. A method of progressive constraints for nonholonomic motion planning [J]. IEEE Transactions on Robotics and Automation. 1998, 14(1): 172-179.
[225] ?gren, P., Svenmarck, P. A new control mode for teleoperated differential drive UGVs [C]. Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, USA, 2007: 5794-5799.
[226] Azizi, A.R.M., Hamiruce, M.M., Kamil, R.A.R. Dead reckoning of a skid steer mobile robot using fuzzy [J]. European Journal of Scientific Research. 2009, 30(2): 305-314.
[227] Talukder, A., Manduchi, R., Castano, R., Owens, K., Matthies, L., Castano, A., Hogg, R. Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles [C]. Proceedings of 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems. Lausanne, Switzerland, 2002: 708-713.
[228] Peng, J., Akella, S. Coordinating multiple robots with kinodynamic constraints along specified paths [J]. International Journal of Robotics Research. 2005, 24(4): 295-310.
[229] Bouhraoua, A., Merah, N., AlDajani, M., ElShafei, M. Design and implementation of an unmanned ground vehicle for security applications [C]. Proceedings of the 7th International Symposium on Mechatronics and its Applications. Sharjah, UAE, 2010.
[230] Chung, W., Fu, L.C., Hsu, S.H. Motion control [Z], Springer Handbook of Robotics. B. Siciliano, Khatib, O., Editors. Berlin: Springer. 2008: 133-159.
[231] Choi, Y., Chung, W.K. PID Trajectory Tracking Control for Mechanical Systems [M]. Lecture Notes in Control and Information Sciences, vol. 298. New York: Springer. 2004.
[232] Alvarez-Ramirez, J., Cervantes, I., Kelly, R. PID regulation of robot manipulators: Stability and performance [J]. Systems and Control Letters. 2000, 41(2): 73-83.
[233] Ziegler, J.B., Nichols, N.B. Optimum settings for automatic controllers [J]. ASME Transactions. 1942, 64: 759-768.
[234]王春民,刘兴明,嵇艳鞠.连续与离散控制系统[M].北京:科学出版社. 2008.
[235]刘金琨.先进PID控制MATLAB仿真(第2版) [M].北京:电子工业出版社. 2004.
[236] Xu, S., Zhou, K., Luo, Z., Yu, F. Study of fuzzy PID control for a semi-track air-cushion vehicle based on power consumption optimization [C]. Proceedings of 2006 IEEE International Conference on Vehicular Electronics and Safety. Shanghai, China, 2006: 440-444.
[237] Wang, Y.T., Chen, Y.C., Lin, M.C. Dynamic object tracking control for a non-holonomic wheeled autonomous robot [J]. Tamkang Journal of Science and Engineering. 2009, 12(3): 339-350.
[238] De Wit, C.C., Díaz, E.O., Perrier, M. Nonlinear control of an underwater vehicle/manipulator with composite dynamics [J]. IEEE Transactions on Control Systems Technology. 2000, 8(6): 948-960.
[239] Isidori, A. Nonlinear Control Systems [M]. 3rd ed. New York: Springer. 1995.
[240] Huang, Y., Cao, Q., Leng, C. The path-tracking controller based on dynamic model with slip for one four-wheeled OMR [J]. Industrial Robot. 2010, 37(2): 193-201.
[241]钱学森,宋健.工程控制论[M].北京:科学出版社. 1980.
[242] Yang, T., Liu, Z., Chen, H., Pei, R. Robust tracking control of mobile robot formation with obstacle avoidance [J]. Journal of Control Science and Engineering. 2007: art. no. 51841.
[243] Yoon, Y., Shin, J., Kim, H.J., Park, Y., Sastry, S. Model-predictive active steering and obstacle avoidance for autonomous ground vehicles [J]. Control Engineering Practice. 2009, 17(7): 741-750.
[244] Abdallah, C., Dawson, D.M., Dorato, P., Jamshidi, M. Survey of robust control for rigid robots [J]. IEEE Control Systems Magazine. 1991, 11(2): 24-30.
[245] Chen, B.S., Lee, C.H., Chang, Y.C. H∞tracking design of uncertain nonlinear SISO systems: Adaptive fuzzy approach [J]. IEEE Transactions on Fuzzy Systems. 1996, 4(1): 32-43.
[246] Park, J., Chung, W.K. Analytic nonlinear H∞inverse-optimal control for Euler-Lagrange system [J]. IEEE Transactions on Robotics and Automation. 2000, 16(6): 847-854.
[247] Ye, J. Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot [J]. Neurocomputing. 2008, 71(7-9): 1561-1565.
[248] Wang, H., Xie, Y. Prediction error based adaptive jacobian tracking of robots with uncertain kinematics and dynamics [J]. IEEE Transactions on Automatic Control. 2009, 54(12): 2889-2894.
[249] Espinosa, F., López, E., Mateos, R., Mazo, M., García, R. Advanced and intelligent control techniques applied to the drive control and path tracking systems on a robotic wheelchair [J]. Autonomous Robots. 2001, 11(2): 137-148.
[250] Narasimhan, M., Singh, S.N. Adaptive optimal control of an autonomous underwater vehicle in the dive plane using dorsal fins [J]. Ocean Engineering. 2006, 33(3-4): 404-416.
[251] Fierro, R., Lewis, F.L. Control of a nonholonomic mobile robot: Backsteppins kinematics into dynamics [J]. Journal of Robotic Systems. 1997, 14(3): 149-163.
[252]丛爽.神经网络、模糊系统及其在运动控制中的应用[M].合肥:中国科学技术大学出版社. 2001.
[253]褚蕾蕾,陈绥阳,周梦.计算智能的数学基础[M].北京:科学出版社. 2002.
[254]窦振中.模糊逻辑控制技术及其应用[M].北京:北京航空航天大学出版社. 1995.
[255] Qiu, R. Some Mathematical Aspects of Fuzzy Systems [D]. Cardiff: Cardiff University. 2004.
[256] Misir, D., Malki, H.A., Chen, G. Design and analysis of a fuzzy proportional-integral-derivative controller [J]. Fuzzy Sets and Systems. 1996, 79(3): 297-314.
[257] Carvajal, J., Chen, G., Ogmen, H. Fuzzy PID controller: Design, performance evaluation, and stability analysis [J]. Information sciences. 2000, 123(3): 249-270.
[258] Mann, G.K.I., Hu, B.G., Gosine, R.G. Analysis of direct action fuzzy PID controller structures [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 1999, 29(3): 371-388.
[259] Sun, Y.L., Er, M.J. Hybrid fuzzy control of robotics systems [J]. IEEE Transactions on Fuzzy Systems. 2004, 12(6): 755-765.
[260] Kodagoda, K.R.S., Wijesoma, W.S., Teoh, E.K. Fuzzy speed and steering control of an AGV [J]. IEEE Transactions on Control Systems Technology. 2002, 10(1): 112-120.
[261]蔡建羡,阮晓刚,甘家飞.两轮自平衡机器人系统建模与模糊自整定PID控制[J].北京工业大学学报. 2009, 35(12): 1603-1607.
[262]朱磊磊,陈军,白晓鸽,杨娜,苏清华.基于曲柄滑块机构原理导航的农业机器人设计[J].农业机械学报. 2009, 40(S1): 33-36.
[263] Wang, J.Z., Ji, J.T., Wang, H.R. Grey prediction fuzzy control of the target tracking system in a robot weapon [J]. Journal of Beijing Institute of Technology (English Edition). 2007, 16(4): 424-429.
[264] Puntunan, S., Parnichkun, M. Online self-tuning precompensation for a PID heading control of a flying robot [J]. International Journal of Advanced Robotic Systems. 2006, 3(4): 323-330.
[265] Chen, H.Y., Wang, J.Q., Zheng, P. Steering control of wheeled armored vehicle with brushless DC motor [J]. Journal of Beijing Institute of Technology (English Edition). 2005, 14(3): 310-313.
[266] Luo, Z., Yu, F., Chen, B.C. Design of a novel semi-tracked air-cushion vehicle for soft terrain [J]. International Journal of Vehicle Design. 2002, 31(1): 112-123.
[267]龙天渝,蔡增基.流体力学泵与风机[M].北京:中国建筑工业出版社. 1999.
[268]喻凡,林逸.汽车系统动力学[M].北京:机械工业出版社. 2005.
[269] Bekker, M.G. Theory of Land Locomotion [M]. Ann Arbor: University of Michigan Press. 1956.
[270] Ray, L.E. Estimation of terrain forces and parameters for rigid-wheeled vehicles [J]. IEEE Transactions on Robotics. 2009, 25(3): 717-726.
[271] Dumond, D.A., Ray, L., Trautmann, E. Evaluation of terrain parameter estimation using a stochastic terrain model [C]. Proceedings of SPIE, 2009: 73321F.
[272] Li, L., Sandu, C. On the impact of cargo weight, vehicle parameters, and terrain characteristics on the prediction of traction for off-road vehicles [J]. Journal of Terramechanics. 2007, 44(3): 221-238.
[273] Golda, D., Iagnemma, K., Dubowsky, S. Probabilistic modeling and analysis of high-speed rough-terrain mobile robots [C]. Proceedings of 2004 IEEE International Conference on Robotics and Automation. New Orleans, USA, 2004: 914-919.
[274] Hutangkabodee, S., Zweiri, Y.H., Seneviratne, L.D., Althoefer, K. Multi-solution problem for track-terrain interaction dynamics and lumped soil parameter identification [Z], Field and Service Robotics. Berlin: Springer. 2006: 517-528.
[275] Tarantola, A. Inverse Problem Theory and Methods for Model Parameter Estimation [M]. Philadelphia:SIAM. 2005.
[276] Le, A.T., Rye, D.C., Durrant-Whyte, H.F. Estimation of track-soil interactions for autonomous tracked vehicles [C]. Proceedings of 1997 IEEE International Conference on Robotics and Automation. Albuquerque, USA, 1997: 1388-1393.
[277] Iagnemma, K., Kang, S., Shibly, H., Dubowsky, S. Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers [J]. IEEE Transactions on Robotics. 2004, 20(5): 921-927.
[278] Hutangkabodee, S., Zweiri, Y.H., Seneviratne, L.D., Althoefer, K. Soil parameter identification for wheel-terrain interaction dynamics and traversability prediction [J]. International Journal of Automation and Computing. 2006, 3(3): 244-251.
[279] Luo, Z., Yu, F. Load distribution control system design for a semi-track air-cushion vehicle [J]. Journal of Terramechanics. 2007, 44(4): 319-325.
[280]许烁,罗哲,周科.基于功耗最优的半履带气垫车模糊PID控制仿真[J].上海交通大学学报. 2007, 41(6): 1026-1030.
[281]刘明树,程悦荪,王登峰,郑联珠,张友坤,王志中.软路面原始谱向有效谱的理论转化方法[J].农业机械学报. 1993, 24(4): 11-17.
[282]刘明树,郑联珠,张友坤.软路面有效不平度的理论研究[J].农业机械学报. 1999, 30(5): 1-4.
[283] Holland, J.E., Holland, C.W. Lightweight skirt assembly for air cushion vehicles [P]. USA patent, WO2005023616. 2004.12.8.
[284] von Frisch, K. The Dance Language and Orientation of Bees [M]. Cambridge, MA: Harvard University Press. 1967.
[285] Beekman, M., Lew, J.B. Foraging in honeybees– when does it pay to dance? [J]. Behavioral Ecology. 2008, 19(2): 255-261.
[286] Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M. The Bees Algorithm– A novel tool for complex optimisation problems [C]. Proceedings of the 2nd International Virtual Conference on Intelligent Production Machines and Systems. Cardiff, UK, 2006: 454-459.
[287] Ghanbarzadeh, A. The Bees Algorithm: A novel optimisation tool [D]. Cardiff. 2007.
[288] Pham, D.T., Ghanbarzadeh, A., Otri, S., Ko?, E. Optimal design of mechanical components using the Bees Algorithm [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2009, 223(5): 1051-1056.
[289] Pham, D.T., Afify, A.A., Koc, E. Manufacturing cell formation using the Bees Algorithm [C]. Proceedings of the 3rd International Virtual Conference on Intelligent Production Machines and Systems. Cardiff, UK, 2007: 523-528.
[290] Xu, S., Ji, Z., Pham, D.T., Yu, F. Bio-inspired binary bees algorithm for a two-level distribution optimisation problem [J]. Journal of Bionic Engineering. 2010, 7(2): 161-167.
[291] Xu, S., Ji, Z., Pham, D.T., Yu, F. Binary Bees Algorithm– Bioinspiration from the foraging mechanism of honeybees to optimize a multiobjective multidimensional assignment problem [J]. Engineering Optimization. Online Publication: http://www.informaworld.com/smpp/content~db=all?content=10.1080/0305215X.2010.542812&jumptype=alert&alerttype=ifirst_author_alert,email.
[292] Pham, D.T., Castellani, M. The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal ofMechanical Engineering Science. 2009, 223(12): 2919-2938.
[293] Ando, K., Miki, M., Hiroyasu, T. Multi-point simulated annealing with adaptive neighborhood [J]. IEICE Transactions on Information and Systems. 2007, E90-D(2): 457-464.
[294] Yang, Z., Tang, K., Yao, X. Self-adaptive differential evolution with neighborhood search [C]. Proceedings of 2008 IEEE Congress on Evolutionary Computation. Washington, D.C., USA, 2008: 1110-1116.
[295] Dhouib, S., Kharrat, A., Chabchoub, H. A variable neighbourhood search method to solve hierarchically engineering design problems [C]. Intelligent Systems and Automation: 2nd Mediterranean Conference on Intelligent Systems and Automation. Zarzis, Tunisia, 2009: 313-317.
[296] Fleszar, K., Osman, I.H., Hindi, K.S. A variable neighbourhood search algorithm for the open vehicle routing problem [J]. European Journal of Operational Research. 2009, 195(3): 803-809.
[297] Quilter, C.G. Foraging activity of the sand beach isopod Scyphax ornatus Dana [J]. New Zealand Journal of Zoology. 1987, 14(4): 433-439.
[298] Heinrich, B. Resource heterogeneity and patterns of movement in foraging bumblebees [J]. Oecologia. 1979, 40(3): 235-245.
[299] Burns, J.G., Dyer, A.G. Diversity of speed-accuracy strategies benefits social insects [J]. Current Biology. 2008, 18(20): R953-R954.
[300] Real, L.A. Uncertainty and pollinator-plant interactions: The foraging behavior of bees and wasps on artificial flowers [J]. Ecology. 1981, 62(1): 20-26.
[301] Rusu, R.B., Sundaresan, A., Morisset, B., Hauser, K., Agrawal, M., Jean-Claude, L., Beetz, M. Leaving Flatland: Effcient real-time three-dimensional perception and motion planning [J]. Journal of Field Robotics. 2009, 26(10): 841-862.
[302] Arbeiter, G., Fischer, J., Verl, A. 3D perception and modelling for manipulation on Care-O-bot 3 [C]. Proceedings of 2010 IEEE International Conference on Robotics and Automation. Anchorage, USA, 2010: in press.
[303] Global Positioning System. [10, November, 2010]. http://www.gps.gov/.
[304]宋小庆.履带车辆电驱动系统及数字控制研究[D].北京:北京理工大学. 2001.
[305]杨耕,罗应立.电机与运动控制系统[M].北京:清华大学出版社. 2006.