微型足球机器人位姿辨识与群智能路径规划技术研究
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摘要
近年来飞速发展的机器人足球比赛系统为人工智能理论的研究提供了一个标准的实验平台。MiroSot是目前开展最为广泛的集控式机器人足球比赛系统之一,一般包括视觉、决策、通信和足球机器人四个子系统,其研究目标是快速准确获取赛场态势并给出合理的决策。相关技术涵盖了机器人学、计算机视觉、传感器融合、实时推理、运动规划与运动控制、无线通信、机器学习、自治智能体和多智能体协作等多个研究领域,引起了各国学者的高度重视。本文重点研究了对MiroSot系统高层决策至关重要的微型足球机器人快速鲁棒位姿辨识技术,并结合近年来新兴的粒子群优化算法研究了群智能路径规划技术。
本文的主要研究内容如下:
(1)研究了MiroSot系统的体系结构、工作原理及其视觉、决策、通信和足球机器人四个子系统的功能与硬件设计,给出了移动机器人路径规划的框架。分析了移动机器人路径规划的问题描述和特点,对传统的移动机器人路径规划算法和新兴的智能路径规划算法进行了总结,比较了各种路径规划算法的优点和不足,探讨了路径规划技术进一步研究的方向。
(2)提出了一种基于较短轴补偿逼近的微型足球机器人位姿辨识算法(SASA)。根据微型足球机器人设计队标色块的对称性特点,提出了一种基于较短轴分割的微型足球机器人色标分块方案,在此基础上给出了基于较短轴补偿逼近的微型足球机器人位姿辨识算法SASA。实验结果表明,SASA算法有效减少了目标机器人位姿辨识的计算量,改进了位姿辨识的计算速度和准确度,提高了系统的实时性。
(3)提出了一种利用相位相关技术进行MiroSot系统微型足球机器人位姿辨识算法(PCGR),并构造了八边形对数极坐标傅里叶变换算法(OLPFFT),提高了运算速度和精度。将分割得到的机器人目标图像和参考图像进行快速离散傅里叶变换后,转换到对数极坐标系下,将笛卡儿坐标空间中图像的旋转和缩放转化为对数极坐标空间中图像的二维平移,进而采用相位相关法得到小车的朝向角。为了提高对数极坐标傅里叶变换的运算速度和精度,构造了一种八边形对数极坐标网格来逼近对数极坐标网格,并给出了八边形对数极坐标网格上的快速傅里叶变换算法(Octa-Log-Polar Fourier Transform, OLPFFT)。实验结果表明,PCGR算法精度高,鲁棒性好。
(4)提出了基于Lotka-Volterra模型的双群协同竞争粒子群优化算法(LVPSO)。最优路径规划问题的本质是优化计算,在LVPSO算法中,针对粒子群优化算法易于出现早熟收敛的问题,借鉴种群生态学中著名的Lotka-Volterra双群协同竞争模型,给出了两种种群协同竞争方案,通过群内和群间竞争增加粒子多样性,提高种群摆脱局部极值的能力。对5个典型基准测试函数进行优化实验表明,LVPSO算法在收敛速度和优化精度方面均有非常好的表现。
(5)提出了一种基于LVPSO和Ferguson样条的MiroSot机器人路径规划算法(LVPSOFS)。利用三次Ferguson样条描述移动机器人路径,将路径规划问题转化为三次样条曲线的参数优化问题,借助LVPSO进行路径优化。实验结果表明,LVPSOFS算法可以有效地实现障碍环境下机器人的无碰撞路径规划,并且实现路径优化,规划路径平滑、合理,利于机器人的运动控制,符合人工规划的意图。
Robot soccer system, one of the hotspots in the field of artificial intelligence (AI)in recent years, is a typical multi-agent system and it provides a standard testingplatform for evaluation of Artificial Intelligence theories and algorithms. FIRA(Federation of International Robot-soccer Association) MiroSot (Micro Robot WorldCup Soccer Tournament) is one of the most popular robot soccer tournaments. MiroSotis a kind of centralized control robot soccer systems which includes four subsystems toget the field situation rapidly and accurately and make reasonable decision, such as:vision subsystem, decision-making subsystem, communication subsystem and robotsubsystem. Many researchers have devoted to this project because it is involved inmany research fields, such as robotics, computer vision, sensor-fusion, real-timereasoning, path planning and motion control, wireless communication, machine learning,autonomous agents, multi-agent collaboration, and so on. Rapid robust gesturerecognition algorithm and swarm-intelligent path planning algorithm are well studied inthis dissertation for their importance to high decision-making.
The main contributions of this dissertation are summarized as follows:
(1) The system architecture and working principle of MiroSot, and the structure,fucction and hardware design of all four MiroSot subsystems, including visionsubsystem, decision-making subsystem, communication subsystem and robotsubsystem, are introduced respectively. A common framework of mobile robotpath planning is proposed. The description and characteristics of mobile robot pathplanning problem, the traditional methods and the up-to-date methods areintroduced, and the advantages and disadvantages of these algorithms are analyzed.Furthermore, the trend of mobile robot path planning is described.
(2) A color tag recognition algorithm based on the shorter axis segmentation andsuccessive approximation technique (SASA) is proposed. By analyzing thesymmetry of color tag, a shorter-axis based successive approximation algorithm isproposed. The experiment results show that the proposed gesture recognitionalgorithm is of lower computing complexity and higher recognition accuracy.
(3) A novel phase-correlation based gesture recognition algorithm (PCGR) in MiroSot is proposed,and the Octa-log-polar Fourier transform (OLPFT) is presented to improve the computingspeed and precision. In the proposed algorithm, the object image and the referenced image are transformed into log-polar coordinate space after FFT, by which rotation and scaling inCartesian coordinate space can be reduced to 2-D translation in log-polar coordinate space.The orientation of the robots can be estimated by phase correlation technique. Octa-log-polarFourier transform (OLPFT) is proposed to estimate the log-polar DFT. The OLPFT estimatesthe DFT on octa-log-polar grid, which is geometrically more similar to the log-polar grid thanpseudo-log-polar grid which can lead to higher accuracy. Experimental results show that theproposed algorithm has high precision and robustness.
(4) A novel Particle Swarm Optimization Algorithm inspired by Lotka-Volterra Model(LVPSO) is proposed in this paper to avoid the premature convergence problem.Path planning is an optimistic computation problem essentially. The famousLotka-Volterra Model in Ecology is introduced into basic particle swarmoptimization algorithm. Two different cooperative-competitive schemes have beendiscussed. The diversity of particles is increased by intraspecific and interspecificcompetition, and the ability for particles to break away from the local extremum isimproved remarkably. The experimental results show that the proposed LVPSOalgorithm can converge in higher speed and higher precision by optimizing fivetypical benchmark functions.
(5) A novel path planning approach using LVPSO and Ferguson splines (LVPSOFS)was proposed to get an optimal smooth path for a micro soccer robot. In theproposed algorithm, a path is described as a string of Ferguson splines. The pathplanning is then equivalent to optimization of parameters of particular cubicFerguson splines. The proposed Particle Swarm Optimization Algorithm inspiredby Lotka-Volterra Model (LVPSO) was introduced to optimize the path for its fastconvergence and global search character. Experimental results prove therationality and practicability of the proposed algorithm, which can getconvergence rapidly, with a collision-avoiding smooth optimal path being plannedfleetly.
本文的主要研究内容如下:
(1)研究了MiroSot系统的体系结构、工作原理及其视觉、决策、通信和足球机器人四个子系统的功能与硬件设计,给出了移动机器人路径规划的框架。分析了移动机器人路径规划的问题描述和特点,对传统的移动机器人路径规划算法和新兴的智能路径规划算法进行了总结,比较了各种路径规划算法的优点和不足,探讨了路径规划技术进一步研究的方向。
(2)提出了一种基于较短轴补偿逼近的微型足球机器人位姿辨识算法(SASA)。根据微型足球机器人设计队标色块的对称性特点,提出了一种基于较短轴分割的微型足球机器人色标分块方案,在此基础上给出了基于较短轴补偿逼近的微型足球机器人位姿辨识算法SASA。实验结果表明,SASA算法有效减少了目标机器人位姿辨识的计算量,改进了位姿辨识的计算速度和准确度,提高了系统的实时性。
(3)提出了一种利用相位相关技术进行MiroSot系统微型足球机器人位姿辨识算法(PCGR),并构造了八边形对数极坐标傅里叶变换算法(OLPFFT),提高了运算速度和精度。将分割得到的机器人目标图像和参考图像进行快速离散傅里叶变换后,转换到对数极坐标系下,将笛卡儿坐标空间中图像的旋转和缩放转化为对数极坐标空间中图像的二维平移,进而采用相位相关法得到小车的朝向角。为了提高对数极坐标傅里叶变换的运算速度和精度,构造了一种八边形对数极坐标网格来逼近对数极坐标网格,并给出了八边形对数极坐标网格上的快速傅里叶变换算法(Octa-Log-Polar Fourier Transform, OLPFFT)。实验结果表明,PCGR算法精度高,鲁棒性好。
(4)提出了基于Lotka-Volterra模型的双群协同竞争粒子群优化算法(LVPSO)。最优路径规划问题的本质是优化计算,在LVPSO算法中,针对粒子群优化算法易于出现早熟收敛的问题,借鉴种群生态学中著名的Lotka-Volterra双群协同竞争模型,给出了两种种群协同竞争方案,通过群内和群间竞争增加粒子多样性,提高种群摆脱局部极值的能力。对5个典型基准测试函数进行优化实验表明,LVPSO算法在收敛速度和优化精度方面均有非常好的表现。
(5)提出了一种基于LVPSO和Ferguson样条的MiroSot机器人路径规划算法(LVPSOFS)。利用三次Ferguson样条描述移动机器人路径,将路径规划问题转化为三次样条曲线的参数优化问题,借助LVPSO进行路径优化。实验结果表明,LVPSOFS算法可以有效地实现障碍环境下机器人的无碰撞路径规划,并且实现路径优化,规划路径平滑、合理,利于机器人的运动控制,符合人工规划的意图。
Robot soccer system, one of the hotspots in the field of artificial intelligence (AI)in recent years, is a typical multi-agent system and it provides a standard testingplatform for evaluation of Artificial Intelligence theories and algorithms. FIRA(Federation of International Robot-soccer Association) MiroSot (Micro Robot WorldCup Soccer Tournament) is one of the most popular robot soccer tournaments. MiroSotis a kind of centralized control robot soccer systems which includes four subsystems toget the field situation rapidly and accurately and make reasonable decision, such as:vision subsystem, decision-making subsystem, communication subsystem and robotsubsystem. Many researchers have devoted to this project because it is involved inmany research fields, such as robotics, computer vision, sensor-fusion, real-timereasoning, path planning and motion control, wireless communication, machine learning,autonomous agents, multi-agent collaboration, and so on. Rapid robust gesturerecognition algorithm and swarm-intelligent path planning algorithm are well studied inthis dissertation for their importance to high decision-making.
The main contributions of this dissertation are summarized as follows:
(1) The system architecture and working principle of MiroSot, and the structure,fucction and hardware design of all four MiroSot subsystems, including visionsubsystem, decision-making subsystem, communication subsystem and robotsubsystem, are introduced respectively. A common framework of mobile robotpath planning is proposed. The description and characteristics of mobile robot pathplanning problem, the traditional methods and the up-to-date methods areintroduced, and the advantages and disadvantages of these algorithms are analyzed.Furthermore, the trend of mobile robot path planning is described.
(2) A color tag recognition algorithm based on the shorter axis segmentation andsuccessive approximation technique (SASA) is proposed. By analyzing thesymmetry of color tag, a shorter-axis based successive approximation algorithm isproposed. The experiment results show that the proposed gesture recognitionalgorithm is of lower computing complexity and higher recognition accuracy.
(3) A novel phase-correlation based gesture recognition algorithm (PCGR) in MiroSot is proposed,and the Octa-log-polar Fourier transform (OLPFT) is presented to improve the computingspeed and precision. In the proposed algorithm, the object image and the referenced image are transformed into log-polar coordinate space after FFT, by which rotation and scaling inCartesian coordinate space can be reduced to 2-D translation in log-polar coordinate space.The orientation of the robots can be estimated by phase correlation technique. Octa-log-polarFourier transform (OLPFT) is proposed to estimate the log-polar DFT. The OLPFT estimatesthe DFT on octa-log-polar grid, which is geometrically more similar to the log-polar grid thanpseudo-log-polar grid which can lead to higher accuracy. Experimental results show that theproposed algorithm has high precision and robustness.
(4) A novel Particle Swarm Optimization Algorithm inspired by Lotka-Volterra Model(LVPSO) is proposed in this paper to avoid the premature convergence problem.Path planning is an optimistic computation problem essentially. The famousLotka-Volterra Model in Ecology is introduced into basic particle swarmoptimization algorithm. Two different cooperative-competitive schemes have beendiscussed. The diversity of particles is increased by intraspecific and interspecificcompetition, and the ability for particles to break away from the local extremum isimproved remarkably. The experimental results show that the proposed LVPSOalgorithm can converge in higher speed and higher precision by optimizing fivetypical benchmark functions.
(5) A novel path planning approach using LVPSO and Ferguson splines (LVPSOFS)was proposed to get an optimal smooth path for a micro soccer robot. In theproposed algorithm, a path is described as a string of Ferguson splines. The pathplanning is then equivalent to optimization of parameters of particular cubicFerguson splines. The proposed Particle Swarm Optimization Algorithm inspiredby Lotka-Volterra Model (LVPSO) was introduced to optimize the path for its fastconvergence and global search character. Experimental results prove therationality and practicability of the proposed algorithm, which can getconvergence rapidly, with a collision-avoiding smooth optimal path being plannedfleetly.
引文
[1]蔡自兴.机器人学[M].北京:清华大学出版社, 2000.
[2] Jacques Ferber. Multi-Agent System, The second Edition[M]. New York: AddisonWesley Longman Inc, 1999.
[3]陈浩耀.面向多机器人编队的基于视觉定位系统研究[D].中国科学技术大学博士论文, 2009.
[4] Y. Hirata, K. Kosuge, H. Asama, et al. Coordinated transportation of a singleobject by multiple mobile robots without position information of each robot [A].Proceedings of 2000 IEEE/RSJ International Conference on Intelligent Robots andSystems [C], Volume 3, Takamatsu, Japan: IEEE, 2000: 2024-2029.
[5] Y. Hirata, Y. Kume, Z. Wang, et al. Handling of a Single Object by MultipleMobile Robots based on Caster-like Dynamics [A]. Proceedings of 2008 IEEEInternational Conference on Robotics and Automation[C], Pasadena, CA, USA:IEEE, 2008: 19-23.
[6]黄闪,蔡鹤皋,谈大龙.面向装配作业的多机器人合作协调系统[J].机器人.1999, 21(1): 50-56.
[7] Pravin Varaiya. Smart cars on smart roads: problems of control [J]. IEEETransactions on Automatic Control, 1993, 38(2): 195-207.
[8] M. Mesbahi, F. Y. Hadaegh. Formation Flying Control of Multiple Spacecraft viaGraphs, Matrix Inequalities, and Switching [J]. Journal of Guidance, Control, andDynamics, 2001, 24(2): 369-377.
[9] Y. Kim, M. Mesbahi. Dual-Spacecraft Formation Flying in Deep Space: OptimalCollision-free Reconfigurations [J]. Journal of Guidance, Control, and Dynamics,2003, 26(2): 375-379.
[10] D. J. Stilwell, B. E. Bishop. Platoons of underwater vehicles [J]. IEEE ControlSystem Magazine, 2000, 20(6): 45-52.
[11]封锡盛,刘永宽.自治水下机器人研究开发的现状和趋势[J].高技术通讯,1999, 13(9):55-59.
[12]孟宪松.多水下机器人系统合作与协调技术研究[D].哈尔滨工程大学博士学位论文, 2006.
[13]黄远灿.国内外军用机器人产业发展现状[J].机器人技术与应用, 2009, (2):25-31.
[14]李实,徐旭明,叶榛等.国际机器人足球比赛及其相关技术[J].机器人. 2000,22(5):420-426.
[15] A. Mackworth. On seeing robots. Computer Vision: Systems, Theory andApplications [M]. Singapore: World Scientific Press, 1993: 1-13.
[16] http://www.fira.net.
[17] Peter Stone. Layered Learning in Multi-agent Systems [D]. Pittsburgh: School ofcomputer science, Carnegie Mellon University, 1998.
[18] http://www.robocup.org.
[19] Peter Stone. Layered Learning in Multi-agent Systems: A Winning Approach toRobotic Soccer[M]. MIT Press, 2000.
[20]吴宪祥,郭宝龙.集控式足球机器人系统关键技术研究[J].计算机工程, 2005,31(17,): 168-170.
[21]王文学,孙萍,徐心和.足球机器人系统结构与关键技术研究[J].控制与决策,2001, 16(2): 233-235.
[22]张祺.基于视觉的机器人足球比赛系统研究[D].广东工业大学博士学位论文,2003.
[23] Kuk-Hyun Han, Kang-Hee Lee, Choon-Kyoung Moon, et al. Robot soccer systemof SOTY 5 for middle league MiroSot[A]. Proceeding of FIRA Robots WorldCongress 2002[C], Seoul: Kaist Press, 2002: 632-635.
[24]方帅,胡英,徐心和.集控式足球机器人视觉子系统的关键技术[J].东北大学学报(自然科学版), 2003, 24(11): 1029-1032.
[25]田国会,尹建芹等.足球机器人视觉系统及其关键问题[J].山东工业大学学报,2002, 32(1): 86-91.
[26] Sargent R., Bailey B., Witty C., et al. The Importance of fast vision in winning theFirst Micro-Robot World Cup Soccer Tournament[J]. Robotics ans AutonomousSystems, 1997, 21(2): 139-147.
[27]吴佩杰,张奇志. MiroSot机器人足球视觉系统图像畸变的几何校正[J].北京机械工业学院学报, 2009, 24(1): 49-52.
[28]魏天滨,孟庆春,庄晓东.一种足球机器人视觉系统非线性畸变的数字校正方法[J].机器人技术与应用, 2004, (4): 37-40.
[29]赵姝颖,佟国峰,徐心和.数字彩色图像样本HIS全息分析方法的研究与实现[J].控制与决策, 1999, 14(S):593-596.
[30]王胜正,施朝健.基于两种色彩空间的颜色选择方法[J].计算机应用与软件.2004, 21(2): 114-116.
[31]陈凤东,洪炳熔等.基于HSI颜色空间的多机器人识别研究[J].哈尔滨工业大学学报, 2004, 36(7): 928-930.
[32]邵丹,韩家伟. YUV与RGB之间的转换[J].长春大学学报, 2004, 14(4): 51-53.
[33]徐大宏,吴敏,曹卫华.足球机器人视觉系统中彩色目标分割方法[J].中南工业大学学报, 2002, 33(4): 428-430.
[34] Chao He, Rong Xiong, Lian-kui Dai. Fast segmentation and identification in visionsystem for soccer robots[A]. Proceedings of the 4th World Congress on IntelligentControl and Automation [C], Shanghai, P. R. China, 2002, Vol.1: 532-536.
[35]欧宗瑛,袁野,张艳珍.基于颜色信息足球机器人视觉跟踪算法[J].大连理工大学学报. 2000, 40(6): 729-732.
[36]陈景航,杨宜民,李健桢等.足球机器人无线通信子系统的研究[J].电路与系统学报, 2006, 11(1): 147-150.
[37]黄卜夫,熊蓉,黄琪等.基于视觉足球机器人无线通讯子系统的设计[J].工程设计学报, 2002, 9(5): 265-267.
[38]刘晓燕,李孝安,段俊花等.基于ZigBee的集控式足球机器人通讯系统[J].计算机测量与控制, 2007, 15(6): 740-744.
[39] Jong-Hwan Kim, Hyun-Sik Shim, Myung-Jin Jung, et al. Cooperative multi-agentrobotic systems: from the robot-soccer perspective[A]. Proceedings ofMIROSOT’97[C], Korea, Seoul: Korea Kaist Publisher, 1997: 3-14.
[40]夏川,曹洋,薛定宇等.基于C8051单片机的足球机器人小车系统设计[J].控制工程, 2003,10(5): 13-21.
[41] Hu Guang, Chen Xin, Cao Weihua, et al. Design of vision-based soccer robotusing DSP [J]. Journal of Harbin Institute of Technology, 2001, 8(3): 239-243.
[42]薛方正.足球机器人对抗策略研究与仿真系统开发[D].东北大学博士学位论文, 2005.
[43] Hyun-Sik Shim, Myung-Jin Jung, Heung-Soo Kim, et al. Development ofVision-based Soccer Robots for Multi-agent Cooperative System[A]. Proceedingsof MIROSOT’97[C], Korea, Seoul: Korea Kaist Publisher, 1997: 29-35.
[44] Jong-Hwan Kim, Kwang-Choon Kim, Dong-Han Kim, et al. Path Planning andRole Selection Mechanism for Soccer Robots [A]. Proceedings of the 1998 IEEEInternational Coference on Robotics & Automation[C], Leuven, Belgium, 1998:3216-3221.
[45]吴丽娟,张春晖,徐心和.足球机器人决策系统推理模型[J].东北大学学报,2001, 22(6): 597-599.
[46] Ju-Jang Lee. Development of micro-robot system for playing soccer games [J].Artificial Life and Robotics, 1999, 3(1): 39-44.
[47] Hyun-Sik Shim, Myung-Jin Jung, Heung-Soo Kim, et al. A hybrid controlstructure for vision based soccer robot system [J]. International Journal ofIntelligent Automation and Soft Computing, 2000, 6(1): 89-101.
[48] Yangmin Li, Wai Ip Lei. A Hybrid Control Approach to Robot SoccerCompetition [A]. Proceedings of the 11th World Congress in Mechanism andMachine Science[C], Tianjin, China: China Machine Press, 2004: 1756-1760.
[49] David Camacho, Fernando Fernandez, Miguel A. Rodelgo. Roboskeleton: Anarchitecture for coordinating robot soccer agents [J]. Engineering Applications ofArtificial Intelligence, 2006, 19(2): 179-188.
[50] Peter Stone, Richard S. Sutton, Gregory Kuhlmann. Reinforcement Learning forRoboCup-Soccer Keepaway [J]. Adaptive Behavior, 2005, 13(3): 165-188.
[51]李实.基于模糊神经网络的多智能体系统学习问题研究[D].清华大学博士学位论文, 2001.
[52] K. G. Jolly, K. P. Ravindran, R. Vijayakumar, et al. Intelligent decision makingin multi-agent robot soccer system through compounded artificial neuralnetworks[J]. Robotics and Autonomous Systems, 2007, 55(7): 589-596.
[53]张祺,杨宜民.基于改进人工势场法的足球机器人避碰控制[J].机器人, 2002,24(1): 12-15.
[54] Hongyan Shi, Ting Feng, Changzhi Sun. Application of Artificial Potential FieldBased on Chaotic Optimization Algorithm in Soccer Game[A]. The 6th WorldCogress on Intelligent Control and Automation[C], Dalian, 2006: 9297-9301.
[55] Dong-Han Kim, Jong-Hwan Kim. A real-time limit-cycle navigation method forfast mobile robots and its application to robot soccer[J]. Robotics and AutonomousSystems, 2003, 42(1): 17-30.
[56]钟碧良,张祺,杨宜民.基于遇障速度法足球机器人的路径规划[J].高技术通讯, 2003, 13(8): 56-60.
[57] Yang Linquan, Luo Zhongwen, Tang Zhonghua, et al. Path Planning Algorithm forMobile Robot Obstacle Avoidance Adopting Bezier Curve Based on GeneticAlgorithm[A]. Proceedings of 2008 Chinese Control and Decision Conference[C].Yantai: IEEE, 2008: 3286-3289.
[58] Li Wang, Yushu Liu, Hongbin Deng, et al. Obstacle-avoidance Path Planning forSoccer Robots Using Particle Swarm Optimization[A]. Proceedings of the 2006IEEE International Conference on Robotics and Biomimetics[C], Kunming: IEEE,2006: 1233-1238.
[59] M. Saska, M. Macas, L. Preucil. Robot Path Planning using Particle SwarmOptimization of Ferguson Splines[A]. ETFA 2006 Proceedings [C]. Piscataway:IEEE, 2006: 833-839.
[60]吴宪祥,郭宝龙,王娟.基于粒子群三次样条优化的移动机器人路径规划算法[J].机器人, 2009, 31(6): 556-560.
[61] K.G. Jolly, R. Sreerama Kumar, R. Vijayakumar. A Bezier curve based pathplanning in a multi-agent robot soccer system without violating the accelerationlimits[J]. Robotics and Autonomous Systems, 2009, 57(1): 23-33.
[62] John J. Leonard, Hugh F. Durrant-Whyte. Mobile Robot Localization by TrackingGeometric Beacons[J]. IEEE Transactions on Robotics and Automation, 1991, 7(3):376-382.
[63]薄喜柱,洪炳熔.动态环境下多移动机器人路径规划的一种新方法[J].机器人,2001, 23(5): 407-410.
[64]唐平,杨宜民.动态二叉树表示环境的A*算法及其在足球机器人路径规划中的实现[J].中国工程科学, 2002, 4(9): 50-53.
[65]吴丽娟,徐心和.基于遗传算法的足球机器人比赛中障碍回避策略的设计机器人[J].机器人, 2001, 23(2): 142-145.
[66]秦元庆,孙德宝,李宁等.基于粒子群算法的移动机器人路径规划[J].机器人,2004, 26(3): 222-225.
[67]唐平.多智能体系统冲突消解与智能机器人动态路径规划研究[D].广东工业大学博士学位论文, 2002.
[68]段勇,杨淮清,崔宝侠等.强化学习在足球机器人基本动作学习中的应用[J].2008, 30(5): 453-459.
[69]李实,徐旭明,叶榛等.机器人足球仿真比赛的Server模型[J].系统仿真学报,2000, 12(2): 138-141.
[70] M. Dorigo, V. Maniezzo, A. Colorni. The ant system: optimization by a colony ofcooperating agents, IEEE Transactions On Systems, Man and CyberneticsPartB ,1996, 26(1): 29-41.
[71] J. Kennedy, R. C. Eberhart. Particle Swarm Optimization[A]. Proceedings of IEEEInternational Conference on Neural Networks, IV[C], IEEE Service Center,Piscataway, NJ, 1995: 1942-1948.
[72]孙波,陈卫东,席玉庚.基于粒子群优化算法的移动机器人全局路径规划[J].控制与决策, 2005, 20(9): 1052-1055.
[1] J. C. Latombe. Robot Motion Planning[M]. Norwell: Kluwer, 1991: 143-176.
[2]孙增圻.智能控制理论与技术[M].北京:清华大学出版社, 1997: 3-45.
[3]王仲民,岳宏.一种移动机器人全局路径规划新型算法[J].机器人, 2003,25(2): 152-155.
[4] T. Lozano-Perez, M. A. Wesley. An algorithm for planning collision-free pathsamong polyhedral obstacles[J]. Communications of the ACM, 1979, 22(10):560-570.
[5] Yang Linquan, Luo Zhongwen, Tang Zhonghua, et al. Path planning algorithm formobile robot obstacle avoidance adopting Bezier curve based on GeneticAlgorithm[A]. Proceedings of CCDC2008[C], Yantai, 2008: 3286-3289.
[6] Xin Chen, Yangmin Li. Smooth path planning of a mobile robot using stochasticparticle swarm optimization[A]. Proceedings of the 2006 IEEE InternationalConference on Mechatronics and Automation[C], Luoyang, 2006: 1722-1727.
[7] S.Baase, A.V.Gelder. Computing Algorithms[M]. Beijing: Higher Education Press,2001: 235-267.
[8]戴光明.避障路径规划的算法研究[D].华中科技大学博士论文, 2004.
[9] J. F. Canny. The complexity of robot motion planning[M]. Cambridge, MA: MITPress, 1988: 168-179.
[10] J.Reif, M.Sharir. Motion planning in the presence of moving obstacles[A]. Proc. Ofthe 26th Annual IEEE Symp. On Foundation of Computer Science[C], Portland,1985: 144-154.
[11] W. E. Howden. The sofa problem[J]. The Computer Journal, 1968, 11(3): 299-301.
[12] Brooks, R. A. Solving the find path problem by good representation of freespace[J]. IEEE Trans. On System Man Cybernetics, 1983, 13(3): 190-197.
[13] Kambhampati S., Davis L. S. Multiresolution path planning for mobile robots[J] .IEEE Journal of Robotics and Automation , 1986, RA-2(3) : 135-145.
[14] J. C. Latombe. Robot motion planning [M]. Boston, MA: Kluwer AcademicPublishers, 1991: 1-200.
[15] C.L. Lawson. Software for C1 surface interpolation[J]. Mathematical Software III(J. R. Rice,ed.), New York:Academic Press, 1977: 161-194.
[16] M. I. Shamos, F. P. Preparata. Computational Geometry: An Introduction[M]. NewYork: Springer-Verlag, 1985.
[17] Waston D. F. Computing the n-Dimensional Delaunay Tessellation withApplication to Voronoi Polytops [J]. The Computer Journal, 1981, 24 (2): 167-172.
[18]窦一康.用逐点插入法自动生成全四边形的自适应有限元网格[J].计算力学学报, 1997, 14(3): 317-323.
[19] THOMPSON K E. Fast and robust Delaunay tessellation in periodic domains[J].International Journal for Numerical Methods in Engineering, 2002, 55(11):1345-1366.
[20] M. V. Anglada. An Improved Incremental Algorithm for Constructing RestrictedDelaunay Triangulations[J]. Computer & Graphics, 1997, 21(2): 215-223.
[21] Christian Sohler. Fast reconstruction of Delaunay triangulations[J]. ComputationalGeometry Theory and Applications. 2005, 31(3): 166-178.
[22] LEWIS B. A., ROBINSON J. S. Triangulation of Planar Regions with Applications[J] . The Computer Journal, 1978, 21(4): 324-332.
[23] Rex A. Dwyer. A Faster Divide-and-Conquer Algorithm for Constructing DelaunayTriangulations [J]. Algorithmica, 1987, 2(1-4): 137-151.
[24] S. Fortune. A sweepline algorithm for Voronoi diagrams[A]. Proceedings of theSecond Annual Symposium on Computational Geometry[C], Yorktown Heights,NY, 1986: 313-322.
[25] V. Domiter, V. Zalik. Sweep-line algorithm for constrained Delaunaytriangulation[J]. International Journal of Geographical Information Science, 2008,22(4): 449-462
[26] L. P. Chew. Constrained Delaunay Triangulations[A]. Proceedings of the AnnualSymposium on Computational Geometry[C], Waterloo,Canada: ACM, 1987:215-222.
[27] L. De Floriani, E. Puppo. An On-Line Algorithm for Constrained DelaunayTriangulation[J]. Computer Vision, Graphics and Image Processing, 1992, 54(4):290-300.
[28] Marcelo Kallmann. Path Planning in Triangulations[A]. Proceedings of theInternational Joint Conference on Artificial Intelligence[C], Edinburgh, Scotland,2005: 49-54.
[29] Nilsson N. J. A mobile automaton: an application of artificial intelligencetechniques[A]. Proc. 1st Int. Joint Conf. on Artificial Intelligence[C], WashingtonD.C., 1969: 509-520.
[30] E. Welzl. Constructing the visibility graph for n line segments in O(n2) time [J].Information Processing Letter. 1985, 20: 167-171.
[31] H. Robnert. Shortest paths in the plane with convex polygonal obstacles[J].Information Processing Letters, 1986, 23(2): 71-76.
[32] Liu Y. H., Arimoto S. Computation of the tangent graph of polygonal obstacles bymoving-line processing [J]. IEEE Transaction on Robotics and Automation, 1994,10(6): 823-830.
[33] I.G. Gowda, D.G. Kirkpatrick, et al. Dynamic Voronoi diagrams [J]. IEEETransactions on Information Theory, 1983, IT-29(10): 724-731.
[34] Canny J F. A Voronoi method for the piano movers problem [A]. Proceedings ofthe IEEE International Conference on Robotics and Automation[C], 1985:530-535.
[35] O. Takahashi, R. J. Schilling. Motion planning in a plane using generalized voronoidiagrams[J]. IEEE Trans.on Robotics and Automation, 1989, 5(2): 143-150.
[36] Choset H. Sensor Based Motion Planning: The Hierarchical Generalized VoronoiGraph[Docotor Thesis],California Institute of Technology, 2006.
[37]严蔚敏,吴伟民.数据结构[M].北京:清华大学出版社, 2007.
[38] E.W.Dijkstra. A Note on Two Problems in Connexion with Graphs [J]. NumerischeMathematic,1959, 1(1): 269-271.
[39] A.Stentz. Optimal and efficient path planning for partially-known environments[A]. Proceedings of the 1994 IEEE International Conference on Robotics andAutomation[C], San Diego, USA: IEEE Computer Society, 1994: 3310-3317.
[40] A.Stentz. The Focussed D* Algorithm for Real-Time Replanning [A]. Proceedingsof the 1995 International Joint Conference on Artificial Intelligence [C], Montreal,Canada, 1995: 1652-1659.
[41] Lumelsky V, Stepanov A. Path Planning Strategies for a Point Mobile AutomationMoving Amongst Unknown Obstacles of Arbitrary Shape[J]. Algorithmic, 1987,3(4): 403-430.
[42] O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots [J].International Journal of Robotic Research, 1986, 5(1): 90-98.
[43] Sato K. Deadlock-free motion planning using Laplace potential field [J]. AdvancedRobotics, 1993, 7(5): 449-461.
[44] Min Gyu Park, Jae Hyun Jeon, Min Cheol Lee. Obstacle avoidance for mobilerobots using artificial potential field approach with simulated annealing [A]. ISIE2001,Pusan, Korea, 2001: 1530-1535.
[45]庄晓东,孟庆春等.复杂环境中基于人工势场优化算法的最有路径规划[J].机器人, 2003, 25(6): 531-535.
[46] Viorel Stoian, Mircea Ivanescu, Elena Stoian, et al. Using Artificial Potential FieldMethods and Fuzzy Logic for Mobile Robot Control [A]. EPE-PEMC 2006,Portoroz, Slovenia, 2006: 385-389.
[47]况菲,王耀南.基于混合人工势场-遗传算法的移动机器人路径规划仿真研究[J].系统仿真学报, 2006, 18(3): 774-777.
[48] Bagchi A., Hatwal H. Fuzzy logic based techniques for motion planning of a robotmanipulator amongst unknown moving obstacles [J]. Robotica, 1992, 10(6):563-573.
[49]庄小东,孟庆春等.动态环境中基于模糊概念的机器人路径搜索方法[J].机器人, 2001, 23(5): 397-399.
[50]张文志,吕恬生.基于改进遗传算法和模糊逻辑控制的移动机器人导航[J].机器人, 2003, 25(1): l-6.
[51] Hopfield J J, Tank D W. Neural Computation of Decisions in OptimizationProblem[J]. Biological Cybernetics, 1985, 52(1): 141-152.
[52]耿兆丰,吴永敢.基于神经网络算法的路径规划[A]. 1992年中国控制与决策学术年会论文集[C],哈尔滨, 1992: 261-265.
[53]孙增圻.智能控制理论与技术[M].北京:清华大学出版社, 1997.
[54] Fierro R, Lewis F L. Control of a Nonholonomic Mobile Robot Using NeuralNetworks[J]. IEEE Trascation on Neural Networks, 1998, 9(4): 589-600.
[55] Payeur P., Le-Huy H., Gosselin C. Robot Path Planning Using Neural Networksand Fuzzy Logic[A].Proceedings of IECON '94[C], Bologna, Italy, 1994, vol.2:800-805.
[56] Meijuan Gao, Jingwen Tian. Path Planning for Mobile Robot Based on ImprovedSimulated Annealing Artificial Neural Network[A]. Proceedings of ICNC 2007,vol.3 [C], Haikou, China, 2007: 8-12.
[57] J.H.Holland. Adaptation in Natural and Artificial Systems[M]. The Unversity ofMichigan Press, 1975.
[58] Gerke M. Genetic Path Planning for Mobile Robots [A]. Proceedings of Americancontrol conference[C]. San Diego, CA, USA, 1999: 596-601.
[59]刘雁飞,裘聿皇.基于两层编码遗传算法的机器人路径规划[J].控制理论与应用, 2000, 17(3): 429-432.
[60]陈刚.复杂环境下路径规划问题的遗传路径规划方法[J].机器人, 2001, 23(1):40-45.
[61]李庆中,顾伟康等.基于遗传算法的移动机器人动态避障路径规划方法[J].模式识别与人工智能, 2002, 15(2): 161-165.
[62]周明,孙树栋,彭炎午.基于遗传模拟退火算法的机器人路径规划[J].航空学报, 1998, 19(1): 118-120.
[63] S.Kirkpatrick. Optimization by Simulated Annealing[J]. Science, 1983, 220(4598):671-680.
[64] R. Storn, K. Price. Differential evolution– A Simple and Efficient Heuristic forGlobal optimization over continuous spaces[J]. Journal of Global Optimization,1997, 11(4): 341-359.
[65] Helder Santos, Jose Mendes, et al. Path Planning Optimization Using theDifferential Evolution Algorithm[J]. Robotica, 2003, 43(3): 382-390.
[66] Zixing Cai, Zhihong Peng. Cooperative Coevolutionary Adaptive GeneticAlgorithm in Path Planning of Cooperative Multi-Mobile Robot Systems[J].Journal of Intelligent and Robotic Systems, 2002, 33(1): 61-71.
[67] Jayasree Chakrabortya, Amit Konara, et al. Cooperative multi-robot path planningusing differential evolution[J]. Journal of Intelligent & Fuzzy Systems, 2009, 20(1):13-27.
[68] M. Dorigo, V. Maniezzo, A. Colorni. The ant system: optimization by a colony ofcooperating agent[J]. IEEE Transactions on Systems.Man and Cybernetics-Part B.1996. 26(1): 1-13.
[69]金飞虎,洪炳熔,高庆吉.基于蚁群算法的自由飞行空间机器人路径规划[J].机器人, 2002, 24(6): 526-529.
[70] K. Gopalakrishnan, S. Ramakrishnan. Optimal Path Planning of Mobile Robotwith Multiple Targets Using Ant Colony Optimization. Smart Engineering Systems,New York, 2006: 25-30.
[71] J. Kennedy, R. Eberhart. Particle swarm optimization[A]. Proceeding of the IEEEInternational Conference on Neural Networks, NewYork, NY, USA: IEEE, 1995:1942-1948.
[72] R. C. Eberhart, Y. Shi. Comparing inertia weights and constriction factors inParticle Swarm Optimization[A]. Proceedings of the Congress on EvolutionaryComputating[C]. San Diego, CA: IEEE, 2000: 84-88.
[73]秦元庆,孙德宝,李宁等.基于粒子群算法的移动机器人路径规划[J].机器人,2004, 26(3): 222-225.
[74] Yuan-qing Qin, De-bao Sun, et al. Path planning for mobile robot using the ParticleSwarm Optimization with mutation operator[A]. Proceedings of the ThirdInternational Conference on Machine Learning and Cybemetics[C], Shanghai,2004: 2473-2478.
[75]孙波,陈卫东,席玉庚.基于粒子群优化算法的移动机器人全局路径规划[J].控制与决策, 2005, 20(9): 1052-1055.
[76] Kavraki L, Latombe J. Randomized preprocessing of configuration space for fastpath planning[A]. Proceedings of the 1994 IEEE International Conference onRobotics and Automation[C], San Diego, USA: IEEE Computer Society, 1994:2138-2139.
[77] LaValle S M. Rapidly-exploring random trees: a new tool for path planning [R].Technical Report TR98-11, Computer Science Dept, Iowa State University, 1998.
[78]成伟明,唐振民等.移动机器人路径规划中的图方法应用综述[J].工程图学学报, 2008, 29(4): 6-14.
[79]石鸿雁,孙昌志.一种基于混沌优化算法的机器人路径规划方法[J].机器人,2005, 27(2): 152-157.
[80] Zeng Dehuai, Xu Gang, et al. Artificial Immune Algorithm based robotobstacle-avoiding path planning[A]. Proceedings of the IEEE InternationalConference on Automation and Logistics[C], Qingdao, China, 2008: 798–803.
[81] E. Masehian, M. R. Amin-Naseri. Sensor-Based Robot Motion Planning - A TabuSearch Approach[J]. Robotics & Automation Magazine, 2008, 15(2): 48–57.
[82]王仲民,岳宏,刘继岩.基于改进模拟退火算法的移动机器人路径规划[J].计算机工程与应用, 2005,41(19): 59-60.
[83] Chunxue Shi,Yingyong Bu Ziguang Li. Path Planning for Deep Sea Mining RobotBased on ACO-PSO Hybrid Algorithm[J]. Proceedings of 2008 InternationalConference on Intelligent Computation Technology and Automation[C], Changsha:IEEE Computer Society, 2008: 125-129.
[84]刘华军,杨静宇,陆建峰等.移动机器人运动规划研究综述[J].中国工程科学,2006, 8(1): 85-94.
[85] Goldberg K. Completeness in robot motion planning[A]. Proceedings of theworkshop on Algorithmic foundations of robotics [C], San Francisco, CA, 1994:419-429.
[1] Sung-Wook Park, Jung-Han Kim, et al. Development of a multiagent system forrobot soccer game[A]. Proceedings of IEEE International Conference on Roboticsand Automation[C], Albuquerque, New Mexico, 1997: 626-631.
[2]方帅,胡英,徐心和.集控式足球机器人视觉子系统的关键技术[J].东北大学学报(自然科学版), 2003, 24(11): 1029-1032.
[3] D. Marr. Vision[M]. San Francisco: W.H. Freeman and Company, 1982.
[4] Kuk-Hyun Han, Kang-Hee Lee, Choon-Kyoung Moon, et al. Robot soccer systemof SOTY 5 for middle league MiroSot[A]. Proceeding of FIRA Robots WorldCongress 2002[C], Seoul: Kaist Press, 2002: 632-635.
[5]胡英,赵姝颖.色标设计与辨识算法研究[J].中国图像图形学报, 2002, 7(12):1291-1295.
[6] HE Chao, XIONG Rong, DAI Lian-kui. Fast Segmentation and Identification inVision-system for Robots[A]. Proceedings of the 4th World Congress onIntelligent Control and Automation[C], Shanghai, P.R.China, 2002: 532-536.
[7]陈凤东,洪炳熔,朱莹.基于HIS颜色空间的多机器人识别研究[J].哈尔滨工业大学学报, 2004, 36(7): 928-930.
[8] F. H. Borsato, F. C. Flores. A Real Time Method to Object Detection TrackingApplied to Robot-Soccer[A]. Proceedings of the 2004 IEEE Conference onCybernetics and Intelligent Systems[C], Singapore: IEEE, 2004: 174-178.
[9] Jun Zhou, Hong-shuang Zhang, Yong Chen. Design of Vision System andRecognition Algorithm in Mirosot[A]. Proceedings of the 2004 IEEE Conferenceon Cybernetics and Intelligent Systems[C], Singapore, 2004: 153-157.
[10] James Bruce, Tucker Balch, Manuela Veloso. Fast and Inexpensive Color imageSegmentation for interactive Robots[A]. Proceedings 2000 IEEE InternationalConference on Intelligent Robots and System[C], Takamatsu, Japan, 2000:2061-2066
[11]薛方正,陈绍军,李祖枢.一种快速位姿检测算法[J].计算机科学, 2007,34(12): 246-247.
[12]彭强,江浩.大场地足球机器人视觉子系统及其识别算法[J].西南交通大学学报, 2005, 40(2): 168-172.
[13] Chris Messom, Nazeer Ahmed, Gourab Sen Gupta. Calibration and auto-tuning ofa realtime RLE vision system[A]. FIRA Robot World Congress 2003[C], Vienna,Austria, 2003.
[14]吴宪祥,郭宝龙. Fira中基于较短轴的色标分块及朝向角辨识算法[J].光电子·激光, 2006, 17(11): 1361-1365.
[15]田国会,尹建芹等.足球机器人视觉系统及其关键问题[J].山东工业大学学报,2002, 32(1): 87-91.
[16]周军,李奎,张伟等.基于角度补偿的色标辨识算法的设计[J].哈尔滨工业大学学报, 2004, 36(7): 969-971.
[17] WANG Yu, WANG Yong, ZU Chun-shan, et al. Design and Modeling ofOmni-Directional Vision System of the Autonomous Soccer Robot[J]. Journal ofOptoelectronics·Laser, 2006, 17(10): 1196-1200.
[1] C. Kuglin, D. Hines. The phase correlation image alignment method. Conferenceon Cybernetics and Society[C]. New York: IEEE, 1975: 163-165.
[2] WU Xian-xiang, GUO Bao-long. FFT-based orientation identification algorithm infira [J]. Journal of Optoelectronics·Laser, 2008, 19(5): 652-655.
[3]吴宪祥,郭宝龙,王娟.一种改进的序列图像自动排序算法[J].光电子·激光,2009, 20(8): 1110-1113.
[4] R. Song, J. Szymanski. Auto-sorting scheme for image ordering applications inimage mosaicing[J]. Electronics Letters, 2008, 44(13): 798-799.
[5]吴宪祥,郭宝龙,王娟.基于相位相关的柱面全景图像自动拼接算法[J].光学学报, 2009, 29(7): 1824-1829.
[6] S. Reddy and B.N. Chatterj. An FFT-based technique for translation, rotation, andscale-invariant image registration[J]. IEEE Trans. Image Process, 1996, 3(8):1266-1270.
[7] Woberg George, Zokai Siavash. Robust Image Registration Using Log-polarTransform[A]. Proceedings of the IEEE International Conference on ImageProcessing[C]. Vancouver, Canada: IEEE, 2000: 493-496.
[8] Y. Keller, A. Averbuch, M. Israeli. Pseudopolar-Based estimation of largetranslations, rotations, and scalings in images[J], IEEE Trans. Image Processing,2005, 14(1): 12-22.
[9] Liu Han-zhou, Guo Bao-long, Feng Zong-zhe. Pesudo-log-polar Fourier Transformfor Image Registration[J]. IEEE Signal Processing Letters, 2006, 13(1): 17-20.
[10] J. Dongarra, F. Sullivan. Guest Editors Introduction to the Top 10 Algorithms[J],Computing in Science & Engineering, 2000, 2(1): 22-23.
[11] L. Briggs, E. Henson. The DFT: An Owner’s Manual for the Discrete FourierTransform[M], Philadelphia: SIAM, 1995.
[12] A. Averbuch, R.R. Coifman, D.L. Donoho, et al. Accurate and fast Polar Fouriertransform[J]. Applied and Computational Harmonic Analysis, 2006, 21(2):145-167.
[13] Karsten Fourmont. Non-equispaced fast Fourier transforms with applications totomography [J]. Journal of Fourier Analysis and Applications, 2003, 9(5):431-450.
[14] G. Beylkin. On the fast Fourier transform of functions with singularities[J].Applied and Computational Harmonic Analysis, 1995, 2(4): 363-381.
[15] A. Dutt, V. Rokhlin. Fast Fourier transforms for nonequispaced data[J]. SIAMJournal on Scientific Computing, 1993, 14(6): 1368-1393.
[16] A.F. Ware. Fast approximate Fourier transform for irregularly spaced data, SIAMReview, 1998, 40(4): 838-856.
[17] J.A. Fessler, B.P. Sutton. Nonuniform fast Fourier transforms using min–maxinterpolation[J]. IEEE Transactions on Signal Processing, 2003, 51(2): 560-574.
[18] L. Greengard, J.Y. Lee. Accelerating the nonuniform fast Fourier transform[J].SIAM Review, 2004, 46(3): 443-454.
[19] M. Fenn, S. Kunis, D. Potts. On the computation of the polar FFT[J]. Applied andComputational Harmonic Analysis, 2007, 22(2): 257-263.
[20] D. Potts, M. Tasche. Numerical stability of nonequispaced fast Fouriertransforms[J]. Journal of Computational and Applied Mathematics, 2008, 222(2):655-674.
[21] R.M. Mersereau, A.V. Oppenheim. Digital reconstruction of multidimensionalsignals from their projections[J]. Proceedings of the IEEE, 1974, 62(10):1319-1338.
[22] P.R. Edholm, G.T. Herman. Linograms in image reconstruction from projections[J].IEEE Transactions on Medical Imaging, 1987, 6(4): 301-307.
[23] W. Lawton. A new Polar Fourier-transform for computer-aided tomography andspotlight synthetic aperture radar[J]. IEEE Transactions on Acoustics, Speech, andSignal Processing, 1988, 36(6): 931-933.
[24] A. Averbuch, Y. Shkolnisky. 3D Fourier based discrete Radon transform[J].Applied and Computational Harmonic Analysis, 2003, 15(1): 33-69.
[25] L.R. Rabiner, R.W. Schafer, C.M. Rader. The Chirp Z-transform algorithm and itsapplication[J]. Bell System Technical Journal, 1969, 48(5): 1249-1292.
[26] D.H. Bailey, P.N. Swarztrauber. The fractional Fourier transform andapplications[J]. SIAM Review, 1991, 33(3): 389-404.
[27] Ofir Harari. A new nearly-Polar FFT and analysis of Fourier-Radon relations indiscrete spaces [D]. Master dissertation, Ben-Gurion University of the Negev,2007.
[28] Xian-xiang Wu, Bao-long Guo, Juan Wang. Octa-Log-Polar Fourier Transform forImage Registration. Proceedings of the 2009 Fifth International Conference onInformation Assurance and Security, Xi’an, China: IEEE, 2009: vol.1: 601-604.
[29]吴宪祥,郭宝龙.基于傅里叶变换的Fira机器人朝向角辨识算法[J].光电子·激光, 2008, 19(5): 652-655.
[30] Xian-xiang Wu, Bao-long Guo. FFT-Based Orientation Recognition Algorithm inMiroSot[A]. Proceedings of the 2008 Eighth International Conference onIntelligent Systems Design and Applications[C], Kaohsiung: IEEE, 2008, vol.2:478-481.
[1]陈宝林.最优化理论与算法(第2版)[M].北京:清华大学出版社, 2005.
[2] Singiresu S. Rao. Engineering Optimization: Theory and Practice (Fourth Edition)[M], Hoboken, New Jersey: Wiley, 2009.
[3] J. J. Hopfield, D.W. Tank.“Neural”computation of decisions in optimizationproblems[J]. Biological Cyberntics, 1985, 52(3): 141-152.
[4] John H. Holland. Adaptation in Natural and Artificial Systems[M]. Ann Abor, MI:The University of Michigan Press, 1975.
[5] D. Dasgupta. Advances in artificial immune systems[J]. IEEE ComputationalIntelligence Maganize, 2006, 1(4): 40-49.
[6] M. Dorigo, V. Maniezzo, A. Colorni. The ant system: optimization by a colony ofcooperating agents, IEEE Transactions On Systems, Man and CyberneticsPartB ,1996, 26(1): 29-41.
[7] S. Kirkpatrick, C. D. Gelatt, et al. Optimization by simulated annealing[J]. Science,1983, 220(4598): 671-680.
[8] S. Kirkpatrick. Optimization by simulated annealing: quantitative studies [J].Journal of Statistical Physics, 1984, 34(5): 975-986.
[9] J. Kennedy, R. C. Eberhart. Particle Swarm Optimization[A]. Proceedings of IEEEInternational Conference on Neural Networks, IV[C], IEEE Service Center,Piscataway, NJ, 1995: 1942-1948.
[10] F. Glover. Tabu Search-Part I [J]. ORSA Journal on Computing, 1989, 1(3):190-206.
[11] F. Glover. Tabu Search-Part II [J]. ORSA Journal on Computing, 1990, 2(1): 4-32.
[12]李兵,蒋慰孙.混沌优化方法及其应用[J].控制理论与应用, 1997, 14(3):285-288.
[13] E. Bonabeau, M. Dorigo, G. Theraulaz. Swarm Intelligence-from Natural toArtificial System[M]. Oxford: Oxford University Press, 1999.
[14] J. Kennedy, R. C. Eberhart. Swarm Intelligence[M]. USA: Academic Press, 2001.
[15] M. Millonas. Swarms, phase transitions, and collective intelligence[A].Computationa Intelligence: A Dynamic System Perspective, IEEE Press,Piscataway, NJ, 1994: 137-151.
[16] C. W. Reynold. Flocks, herds, and schools: a distributed behavioral model[J].Computer Graphics, 1987, 21(4): 25-34.
[17] Edward O. Wilson. Sociobiology: The New Synthesis. Cambridge, Mass.: BelknapPress, 1975.
[18] Y. Shi, R.C. Eberhart. A modified particle swarm Optimizer[A]. Proceedings of theIEEE Congress on Evolutionary Coputation[C], Piscataway, NJ: IEEE Press, 1998:69-73.
[19] R. C. Eberhart. Y. Shi. Comparing inertia weights and constriction factors inParticle Swarm Optimization[A]. Proceedings of the Congress on EvolutionaryComputation[C]. San Diego, CA: IEEE, 2000: 84-88.
[20] M. Clerc , J. Kennedy. The particle swarm-explosion, stability and convergence ina multidimensional complex space [J]. IEEE Transactions on EvolutionaryComputation, 2002, 6(1): 58-73.
[21] A. Carlisle, G. Dozier. Adapting particle swarm optimization to dynamicenvironments[A]. Proceedings of International Conference on ArtificialIntelligence [C], Las Vegas, Nevada, USA, 2000: 429-434.
[22] T. M. Blackwell. Particle swarms and population diversity[J]. Soft Computing- AFusion of Foundations, Methodologies and Applications, 2005, 9(11): 793-802.
[23] T. Blackwell, J. Branke. Multiswarms, Exclusion, and Anti-Convergence inDynamic Environments[J]. IEEE Transactions on Evolutionary Computation, 2006,10(4): 459-472.
[24] T. M. Blackwell, P. J. Bentley. Dynamic search with charged swarms[A].Proceedings of the Genetic and Evolutionary Computation Conference 2002[C],New York, USA: Morgan Kaufmann, 2002: 19-26.
[25] Liping Zhang, Huanjun Yu, Shangxu Hu. A new approach to improve ParticleSwarm Optimization. Lecture Notes in Computer Science (LNCS) No.2723:Proceedings of the Genetic and Evolutionary Computation Conference2003(GECCO 2003), Chicago, IL, USA, 2003: 134-142.
[26]高浩,冷文浩,须文波.一种全局收敛的PSO算法及其收敛分析[J].控制与决策. 2009, 24(2): 196-201.
[27] X. Hu, R. C. Eberhart. Adaptive Particle Swarm Optimization: Detection andResponse to Dynamic Systems. Proceedings of IEEE Congress on EvolutionaryComputation(CEC2002), Honolulu, Hawaii USA: IEEE, 2002: 1666-1670.
[28]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报, 2004, 32(3):416-420.
[29]高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学, 2004, 31(8): 13-15.
[30] J. Kennedy. Small worlds and mega-minds:effects of neighborhood topology onparticle swarm performance[A]. Proceedings of the 1999 IEEE Congress onEvolutionary Computation [C], Piscataway, NJ: IEEE Press, 1999: 1931-1938.
[31] J. Kennedy, R. Mendes. Population Structure and Particle Swarm Performance [A].Proceedings of the 2002 World Congress on Computational Intelligence[C],Hawaii, USA: IEEE Press, 2002: 1671-1676.
[32] R. Mendes, J. Kennedy, J. Neves. The Fully Informed Particle Swarm: Simpler,Maybe Better [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3):204-210.
[33] J. Branke. Memory enhanced evolutionary algorithms for changing optimizationproblems[A]. Proceedings of the 1999 Congress on Evolutionary Computation[C], Washington D.C., USA: IEEE Press, 1999, vol.3: 1875-1882.
[34] Y. Yan, B. Guo. Particle Swarm Optimization Inspired by r- and K-Selection inEcology[A]. Proceedings of 2008 IEEE Congress on Evolutionary Computation(CEC 2008)[C], Hong Kong, China: IEEE Press, 2008: 1117-1123.
[35]李博,杨持等.生态学[M].北京:高等教育出版社, 2000.
[36]孙濡泳,李庆芬,牛翠娟等.基础生态学[M].北京:高等教育出版社, 2002.
[37]达尔文著,周建人,叶笃庄,方宗熙译.物种起源[M].北京:商务印书馆,1995.
[38] Karin Kiontke, Antoine Barrière, Irina Kolotuev et.al. Trends, Stasis, and Drift inthe Evolution of Nematode Vulva Development [J]. Current Biology, 2007, 17(22):1925-1937.
[39]张琴. Lotka-Volterra模型的若干问题[D].吉林大学硕士学位论文, 2006.
[40]高鹰,姚振坚,谢胜利.基于种群密度的粒子群优化算法[J].系统工程与电子技术, 2006, 28(6): 922-924.
[41]尚玉昌,蔡晓明.普通生态学[M] .北京:北京大学出版社, 1996.
[42]吴宪祥,郭宝龙,王娟.基于Lotka-Volterra模型的双群协同竞争粒子群优化算法[J].控制与决策, 2009(已录用).
[43]闫允一.粒子群优化及其在图像处理中的应用研究[D].西安电子科技大学博士学位论文, 2008.
[1] J.A.P. Kjellander. Smoothing of cubic parametric splines [J]. Computer-AidedDesign, 1983, 15(3): 175-179.
[2]朱安风.基于双曲函数的H-Bézier和Ferguson曲线[D].合肥工业大学硕士学位论文, 2007: 22-29.
[3] James Ferguson. Multivariable Curve Interpolation [J]. Journal of the Associationfor Computing Machinery, 1964, 22(2): 221-228.
[4] J. Ye, R. Qu. Fairing of parametric cubic splines [J]. Mathematical and ComputerModelling. 1999, 30(5): 121-131.
[5]刘华军,杨静宇,陆建峰,等.移动机器人运动规划研究综述[J].中国工程科学,2006, 8(1): 85-94.
[6]马兆青,袁曾任.基于栅格方法的移动机器人实时导航和避障[J].机器人,1996, 18(6): 344-348.
[7] Keron Y, Borenstein J . Potential field methods and their inherent limitations formobile robot navigation [A] . Proceedings of the International Conference onRobotics and Automation [C]. California, 1991. 1398-1404.
[8]秦元庆,孙德宝,李宁等.基于粒子群算法的移动机器人路径规划[J].机器人,2004, 26(3): 222-225.
[9] Lumelsky V, Stepanov A. Path Planning Strategies for a Point Mobile AutomationMoving Amongst Unknown Obstacles of Arbitrary Shape[J]. Algorithmic, 1987, 3(4): 403-430.
[10] Marcelo Kallmann. Path Planning in Triangulation [A]. Proceedings of theworkshop on reasoning, representation, and learning in computer games[C].Edinburgh, Scotland, 2005: 49-54.
[11]梅昊,田彦涛,祖丽楠.动态环境下机器人路径规划的混合蚁群算法[J].吉林大学学报(信息科学版), 2006, 24(2) :148-152.
[12] Yang Linquan, Luo Zhongwen, Tang Zhonghua, et al. Path Planning Algorithm forMobile Robot Obstacle Avoidance Adopting Bezier Curve Based on GeneticAlgorithm[A]. Proceedings of 2008 Chinese Control and Decision Conference[C].Yantai: IEEE, 2008:3286-3289.
[13] K.G. Jollyb, R. Sreerama Kumar, R. Vijayakumar. A Bezier curve based pathplanning in a multi-agent robot soccer system without violating the accelerationlimits[J]. Robotics and Autonomous Systems, 2009, 57(1): 23-33.
[14] R. C. Eberhart. Y. Shi. Comparing inertia weights and constriction factors inParticle Swarm Optimization[A]. Proceedings of the Congress on EvolutionaryComputation[C]. San Diego, CA: IEEE, 2000: 84-88.
[15] M. Clerc, J. Kennedy. The particle swarm-explosion, stability and convergence ina multidimensional complex space [J]. IEEE Transactions on EvolutionaryComputation, 2002, 6(1): 58-73.
[16] Xin Chen, Yangmin Li. Smooth Path Planning of a Mobile Robot Using StochasticParticle Swarm Optimization[A]. Proceedings of the 2006 IEEE InternationalConference on Mechatronics and Automation[C]. Luoyang: IEEE, 2006:1722-1727.
[17] Y.Q. Qin, D.B. Sun, M. Li, et al. Path Planning for Mobile Robot Using theParticle Swarm Optimization with Mutation Operator [A]. Proceedings ofInternational Conference on Machine Learning and Cybernetics, vol. 4 [C], 2004:2473-2478.
[18] Li Wang, Yushu Liu, Hongbin Deng, et al. Obstacle-avoidance Path Planning forSoccer Robots Using Particle Swarm Optimization[A]. Proceedings of the 2006IEEE International Conference on Robotics and Biomimetics[C], Kunming: IEEE,2006: 1233-1238.
[19]吴宪祥,郭宝龙,王娟.基于粒子群三次样条优化的移动机器人路径规划算法[J].机器人, 2009, 31(6): 556-560.
[20] M. Saska, M. Macas, L. Preucil. Robot Path Planning using Particle SwarmOptimization of Ferguson Splines[A]. ETFA 2006 Proceedings [C]. Piscataway:IEEE, 2006: 833-839.
[21]吴宪祥,郭宝龙,王娟.基于Lotka-Volterra模型的双群协同竞争粒子群优化算法[J].控制与决策, 2009(已录用).
[2] Jacques Ferber. Multi-Agent System, The second Edition[M]. New York: AddisonWesley Longman Inc, 1999.
[3]陈浩耀.面向多机器人编队的基于视觉定位系统研究[D].中国科学技术大学博士论文, 2009.
[4] Y. Hirata, K. Kosuge, H. Asama, et al. Coordinated transportation of a singleobject by multiple mobile robots without position information of each robot [A].Proceedings of 2000 IEEE/RSJ International Conference on Intelligent Robots andSystems [C], Volume 3, Takamatsu, Japan: IEEE, 2000: 2024-2029.
[5] Y. Hirata, Y. Kume, Z. Wang, et al. Handling of a Single Object by MultipleMobile Robots based on Caster-like Dynamics [A]. Proceedings of 2008 IEEEInternational Conference on Robotics and Automation[C], Pasadena, CA, USA:IEEE, 2008: 19-23.
[6]黄闪,蔡鹤皋,谈大龙.面向装配作业的多机器人合作协调系统[J].机器人.1999, 21(1): 50-56.
[7] Pravin Varaiya. Smart cars on smart roads: problems of control [J]. IEEETransactions on Automatic Control, 1993, 38(2): 195-207.
[8] M. Mesbahi, F. Y. Hadaegh. Formation Flying Control of Multiple Spacecraft viaGraphs, Matrix Inequalities, and Switching [J]. Journal of Guidance, Control, andDynamics, 2001, 24(2): 369-377.
[9] Y. Kim, M. Mesbahi. Dual-Spacecraft Formation Flying in Deep Space: OptimalCollision-free Reconfigurations [J]. Journal of Guidance, Control, and Dynamics,2003, 26(2): 375-379.
[10] D. J. Stilwell, B. E. Bishop. Platoons of underwater vehicles [J]. IEEE ControlSystem Magazine, 2000, 20(6): 45-52.
[11]封锡盛,刘永宽.自治水下机器人研究开发的现状和趋势[J].高技术通讯,1999, 13(9):55-59.
[12]孟宪松.多水下机器人系统合作与协调技术研究[D].哈尔滨工程大学博士学位论文, 2006.
[13]黄远灿.国内外军用机器人产业发展现状[J].机器人技术与应用, 2009, (2):25-31.
[14]李实,徐旭明,叶榛等.国际机器人足球比赛及其相关技术[J].机器人. 2000,22(5):420-426.
[15] A. Mackworth. On seeing robots. Computer Vision: Systems, Theory andApplications [M]. Singapore: World Scientific Press, 1993: 1-13.
[16] http://www.fira.net.
[17] Peter Stone. Layered Learning in Multi-agent Systems [D]. Pittsburgh: School ofcomputer science, Carnegie Mellon University, 1998.
[18] http://www.robocup.org.
[19] Peter Stone. Layered Learning in Multi-agent Systems: A Winning Approach toRobotic Soccer[M]. MIT Press, 2000.
[20]吴宪祥,郭宝龙.集控式足球机器人系统关键技术研究[J].计算机工程, 2005,31(17,): 168-170.
[21]王文学,孙萍,徐心和.足球机器人系统结构与关键技术研究[J].控制与决策,2001, 16(2): 233-235.
[22]张祺.基于视觉的机器人足球比赛系统研究[D].广东工业大学博士学位论文,2003.
[23] Kuk-Hyun Han, Kang-Hee Lee, Choon-Kyoung Moon, et al. Robot soccer systemof SOTY 5 for middle league MiroSot[A]. Proceeding of FIRA Robots WorldCongress 2002[C], Seoul: Kaist Press, 2002: 632-635.
[24]方帅,胡英,徐心和.集控式足球机器人视觉子系统的关键技术[J].东北大学学报(自然科学版), 2003, 24(11): 1029-1032.
[25]田国会,尹建芹等.足球机器人视觉系统及其关键问题[J].山东工业大学学报,2002, 32(1): 86-91.
[26] Sargent R., Bailey B., Witty C., et al. The Importance of fast vision in winning theFirst Micro-Robot World Cup Soccer Tournament[J]. Robotics ans AutonomousSystems, 1997, 21(2): 139-147.
[27]吴佩杰,张奇志. MiroSot机器人足球视觉系统图像畸变的几何校正[J].北京机械工业学院学报, 2009, 24(1): 49-52.
[28]魏天滨,孟庆春,庄晓东.一种足球机器人视觉系统非线性畸变的数字校正方法[J].机器人技术与应用, 2004, (4): 37-40.
[29]赵姝颖,佟国峰,徐心和.数字彩色图像样本HIS全息分析方法的研究与实现[J].控制与决策, 1999, 14(S):593-596.
[30]王胜正,施朝健.基于两种色彩空间的颜色选择方法[J].计算机应用与软件.2004, 21(2): 114-116.
[31]陈凤东,洪炳熔等.基于HSI颜色空间的多机器人识别研究[J].哈尔滨工业大学学报, 2004, 36(7): 928-930.
[32]邵丹,韩家伟. YUV与RGB之间的转换[J].长春大学学报, 2004, 14(4): 51-53.
[33]徐大宏,吴敏,曹卫华.足球机器人视觉系统中彩色目标分割方法[J].中南工业大学学报, 2002, 33(4): 428-430.
[34] Chao He, Rong Xiong, Lian-kui Dai. Fast segmentation and identification in visionsystem for soccer robots[A]. Proceedings of the 4th World Congress on IntelligentControl and Automation [C], Shanghai, P. R. China, 2002, Vol.1: 532-536.
[35]欧宗瑛,袁野,张艳珍.基于颜色信息足球机器人视觉跟踪算法[J].大连理工大学学报. 2000, 40(6): 729-732.
[36]陈景航,杨宜民,李健桢等.足球机器人无线通信子系统的研究[J].电路与系统学报, 2006, 11(1): 147-150.
[37]黄卜夫,熊蓉,黄琪等.基于视觉足球机器人无线通讯子系统的设计[J].工程设计学报, 2002, 9(5): 265-267.
[38]刘晓燕,李孝安,段俊花等.基于ZigBee的集控式足球机器人通讯系统[J].计算机测量与控制, 2007, 15(6): 740-744.
[39] Jong-Hwan Kim, Hyun-Sik Shim, Myung-Jin Jung, et al. Cooperative multi-agentrobotic systems: from the robot-soccer perspective[A]. Proceedings ofMIROSOT’97[C], Korea, Seoul: Korea Kaist Publisher, 1997: 3-14.
[40]夏川,曹洋,薛定宇等.基于C8051单片机的足球机器人小车系统设计[J].控制工程, 2003,10(5): 13-21.
[41] Hu Guang, Chen Xin, Cao Weihua, et al. Design of vision-based soccer robotusing DSP [J]. Journal of Harbin Institute of Technology, 2001, 8(3): 239-243.
[42]薛方正.足球机器人对抗策略研究与仿真系统开发[D].东北大学博士学位论文, 2005.
[43] Hyun-Sik Shim, Myung-Jin Jung, Heung-Soo Kim, et al. Development ofVision-based Soccer Robots for Multi-agent Cooperative System[A]. Proceedingsof MIROSOT’97[C], Korea, Seoul: Korea Kaist Publisher, 1997: 29-35.
[44] Jong-Hwan Kim, Kwang-Choon Kim, Dong-Han Kim, et al. Path Planning andRole Selection Mechanism for Soccer Robots [A]. Proceedings of the 1998 IEEEInternational Coference on Robotics & Automation[C], Leuven, Belgium, 1998:3216-3221.
[45]吴丽娟,张春晖,徐心和.足球机器人决策系统推理模型[J].东北大学学报,2001, 22(6): 597-599.
[46] Ju-Jang Lee. Development of micro-robot system for playing soccer games [J].Artificial Life and Robotics, 1999, 3(1): 39-44.
[47] Hyun-Sik Shim, Myung-Jin Jung, Heung-Soo Kim, et al. A hybrid controlstructure for vision based soccer robot system [J]. International Journal ofIntelligent Automation and Soft Computing, 2000, 6(1): 89-101.
[48] Yangmin Li, Wai Ip Lei. A Hybrid Control Approach to Robot SoccerCompetition [A]. Proceedings of the 11th World Congress in Mechanism andMachine Science[C], Tianjin, China: China Machine Press, 2004: 1756-1760.
[49] David Camacho, Fernando Fernandez, Miguel A. Rodelgo. Roboskeleton: Anarchitecture for coordinating robot soccer agents [J]. Engineering Applications ofArtificial Intelligence, 2006, 19(2): 179-188.
[50] Peter Stone, Richard S. Sutton, Gregory Kuhlmann. Reinforcement Learning forRoboCup-Soccer Keepaway [J]. Adaptive Behavior, 2005, 13(3): 165-188.
[51]李实.基于模糊神经网络的多智能体系统学习问题研究[D].清华大学博士学位论文, 2001.
[52] K. G. Jolly, K. P. Ravindran, R. Vijayakumar, et al. Intelligent decision makingin multi-agent robot soccer system through compounded artificial neuralnetworks[J]. Robotics and Autonomous Systems, 2007, 55(7): 589-596.
[53]张祺,杨宜民.基于改进人工势场法的足球机器人避碰控制[J].机器人, 2002,24(1): 12-15.
[54] Hongyan Shi, Ting Feng, Changzhi Sun. Application of Artificial Potential FieldBased on Chaotic Optimization Algorithm in Soccer Game[A]. The 6th WorldCogress on Intelligent Control and Automation[C], Dalian, 2006: 9297-9301.
[55] Dong-Han Kim, Jong-Hwan Kim. A real-time limit-cycle navigation method forfast mobile robots and its application to robot soccer[J]. Robotics and AutonomousSystems, 2003, 42(1): 17-30.
[56]钟碧良,张祺,杨宜民.基于遇障速度法足球机器人的路径规划[J].高技术通讯, 2003, 13(8): 56-60.
[57] Yang Linquan, Luo Zhongwen, Tang Zhonghua, et al. Path Planning Algorithm forMobile Robot Obstacle Avoidance Adopting Bezier Curve Based on GeneticAlgorithm[A]. Proceedings of 2008 Chinese Control and Decision Conference[C].Yantai: IEEE, 2008: 3286-3289.
[58] Li Wang, Yushu Liu, Hongbin Deng, et al. Obstacle-avoidance Path Planning forSoccer Robots Using Particle Swarm Optimization[A]. Proceedings of the 2006IEEE International Conference on Robotics and Biomimetics[C], Kunming: IEEE,2006: 1233-1238.
[59] M. Saska, M. Macas, L. Preucil. Robot Path Planning using Particle SwarmOptimization of Ferguson Splines[A]. ETFA 2006 Proceedings [C]. Piscataway:IEEE, 2006: 833-839.
[60]吴宪祥,郭宝龙,王娟.基于粒子群三次样条优化的移动机器人路径规划算法[J].机器人, 2009, 31(6): 556-560.
[61] K.G. Jolly, R. Sreerama Kumar, R. Vijayakumar. A Bezier curve based pathplanning in a multi-agent robot soccer system without violating the accelerationlimits[J]. Robotics and Autonomous Systems, 2009, 57(1): 23-33.
[62] John J. Leonard, Hugh F. Durrant-Whyte. Mobile Robot Localization by TrackingGeometric Beacons[J]. IEEE Transactions on Robotics and Automation, 1991, 7(3):376-382.
[63]薄喜柱,洪炳熔.动态环境下多移动机器人路径规划的一种新方法[J].机器人,2001, 23(5): 407-410.
[64]唐平,杨宜民.动态二叉树表示环境的A*算法及其在足球机器人路径规划中的实现[J].中国工程科学, 2002, 4(9): 50-53.
[65]吴丽娟,徐心和.基于遗传算法的足球机器人比赛中障碍回避策略的设计机器人[J].机器人, 2001, 23(2): 142-145.
[66]秦元庆,孙德宝,李宁等.基于粒子群算法的移动机器人路径规划[J].机器人,2004, 26(3): 222-225.
[67]唐平.多智能体系统冲突消解与智能机器人动态路径规划研究[D].广东工业大学博士学位论文, 2002.
[68]段勇,杨淮清,崔宝侠等.强化学习在足球机器人基本动作学习中的应用[J].2008, 30(5): 453-459.
[69]李实,徐旭明,叶榛等.机器人足球仿真比赛的Server模型[J].系统仿真学报,2000, 12(2): 138-141.
[70] M. Dorigo, V. Maniezzo, A. Colorni. The ant system: optimization by a colony ofcooperating agents, IEEE Transactions On Systems, Man and CyberneticsPartB ,1996, 26(1): 29-41.
[71] J. Kennedy, R. C. Eberhart. Particle Swarm Optimization[A]. Proceedings of IEEEInternational Conference on Neural Networks, IV[C], IEEE Service Center,Piscataway, NJ, 1995: 1942-1948.
[72]孙波,陈卫东,席玉庚.基于粒子群优化算法的移动机器人全局路径规划[J].控制与决策, 2005, 20(9): 1052-1055.
[1] J. C. Latombe. Robot Motion Planning[M]. Norwell: Kluwer, 1991: 143-176.
[2]孙增圻.智能控制理论与技术[M].北京:清华大学出版社, 1997: 3-45.
[3]王仲民,岳宏.一种移动机器人全局路径规划新型算法[J].机器人, 2003,25(2): 152-155.
[4] T. Lozano-Perez, M. A. Wesley. An algorithm for planning collision-free pathsamong polyhedral obstacles[J]. Communications of the ACM, 1979, 22(10):560-570.
[5] Yang Linquan, Luo Zhongwen, Tang Zhonghua, et al. Path planning algorithm formobile robot obstacle avoidance adopting Bezier curve based on GeneticAlgorithm[A]. Proceedings of CCDC2008[C], Yantai, 2008: 3286-3289.
[6] Xin Chen, Yangmin Li. Smooth path planning of a mobile robot using stochasticparticle swarm optimization[A]. Proceedings of the 2006 IEEE InternationalConference on Mechatronics and Automation[C], Luoyang, 2006: 1722-1727.
[7] S.Baase, A.V.Gelder. Computing Algorithms[M]. Beijing: Higher Education Press,2001: 235-267.
[8]戴光明.避障路径规划的算法研究[D].华中科技大学博士论文, 2004.
[9] J. F. Canny. The complexity of robot motion planning[M]. Cambridge, MA: MITPress, 1988: 168-179.
[10] J.Reif, M.Sharir. Motion planning in the presence of moving obstacles[A]. Proc. Ofthe 26th Annual IEEE Symp. On Foundation of Computer Science[C], Portland,1985: 144-154.
[11] W. E. Howden. The sofa problem[J]. The Computer Journal, 1968, 11(3): 299-301.
[12] Brooks, R. A. Solving the find path problem by good representation of freespace[J]. IEEE Trans. On System Man Cybernetics, 1983, 13(3): 190-197.
[13] Kambhampati S., Davis L. S. Multiresolution path planning for mobile robots[J] .IEEE Journal of Robotics and Automation , 1986, RA-2(3) : 135-145.
[14] J. C. Latombe. Robot motion planning [M]. Boston, MA: Kluwer AcademicPublishers, 1991: 1-200.
[15] C.L. Lawson. Software for C1 surface interpolation[J]. Mathematical Software III(J. R. Rice,ed.), New York:Academic Press, 1977: 161-194.
[16] M. I. Shamos, F. P. Preparata. Computational Geometry: An Introduction[M]. NewYork: Springer-Verlag, 1985.
[17] Waston D. F. Computing the n-Dimensional Delaunay Tessellation withApplication to Voronoi Polytops [J]. The Computer Journal, 1981, 24 (2): 167-172.
[18]窦一康.用逐点插入法自动生成全四边形的自适应有限元网格[J].计算力学学报, 1997, 14(3): 317-323.
[19] THOMPSON K E. Fast and robust Delaunay tessellation in periodic domains[J].International Journal for Numerical Methods in Engineering, 2002, 55(11):1345-1366.
[20] M. V. Anglada. An Improved Incremental Algorithm for Constructing RestrictedDelaunay Triangulations[J]. Computer & Graphics, 1997, 21(2): 215-223.
[21] Christian Sohler. Fast reconstruction of Delaunay triangulations[J]. ComputationalGeometry Theory and Applications. 2005, 31(3): 166-178.
[22] LEWIS B. A., ROBINSON J. S. Triangulation of Planar Regions with Applications[J] . The Computer Journal, 1978, 21(4): 324-332.
[23] Rex A. Dwyer. A Faster Divide-and-Conquer Algorithm for Constructing DelaunayTriangulations [J]. Algorithmica, 1987, 2(1-4): 137-151.
[24] S. Fortune. A sweepline algorithm for Voronoi diagrams[A]. Proceedings of theSecond Annual Symposium on Computational Geometry[C], Yorktown Heights,NY, 1986: 313-322.
[25] V. Domiter, V. Zalik. Sweep-line algorithm for constrained Delaunaytriangulation[J]. International Journal of Geographical Information Science, 2008,22(4): 449-462
[26] L. P. Chew. Constrained Delaunay Triangulations[A]. Proceedings of the AnnualSymposium on Computational Geometry[C], Waterloo,Canada: ACM, 1987:215-222.
[27] L. De Floriani, E. Puppo. An On-Line Algorithm for Constrained DelaunayTriangulation[J]. Computer Vision, Graphics and Image Processing, 1992, 54(4):290-300.
[28] Marcelo Kallmann. Path Planning in Triangulations[A]. Proceedings of theInternational Joint Conference on Artificial Intelligence[C], Edinburgh, Scotland,2005: 49-54.
[29] Nilsson N. J. A mobile automaton: an application of artificial intelligencetechniques[A]. Proc. 1st Int. Joint Conf. on Artificial Intelligence[C], WashingtonD.C., 1969: 509-520.
[30] E. Welzl. Constructing the visibility graph for n line segments in O(n2) time [J].Information Processing Letter. 1985, 20: 167-171.
[31] H. Robnert. Shortest paths in the plane with convex polygonal obstacles[J].Information Processing Letters, 1986, 23(2): 71-76.
[32] Liu Y. H., Arimoto S. Computation of the tangent graph of polygonal obstacles bymoving-line processing [J]. IEEE Transaction on Robotics and Automation, 1994,10(6): 823-830.
[33] I.G. Gowda, D.G. Kirkpatrick, et al. Dynamic Voronoi diagrams [J]. IEEETransactions on Information Theory, 1983, IT-29(10): 724-731.
[34] Canny J F. A Voronoi method for the piano movers problem [A]. Proceedings ofthe IEEE International Conference on Robotics and Automation[C], 1985:530-535.
[35] O. Takahashi, R. J. Schilling. Motion planning in a plane using generalized voronoidiagrams[J]. IEEE Trans.on Robotics and Automation, 1989, 5(2): 143-150.
[36] Choset H. Sensor Based Motion Planning: The Hierarchical Generalized VoronoiGraph[Docotor Thesis],California Institute of Technology, 2006.
[37]严蔚敏,吴伟民.数据结构[M].北京:清华大学出版社, 2007.
[38] E.W.Dijkstra. A Note on Two Problems in Connexion with Graphs [J]. NumerischeMathematic,1959, 1(1): 269-271.
[39] A.Stentz. Optimal and efficient path planning for partially-known environments[A]. Proceedings of the 1994 IEEE International Conference on Robotics andAutomation[C], San Diego, USA: IEEE Computer Society, 1994: 3310-3317.
[40] A.Stentz. The Focussed D* Algorithm for Real-Time Replanning [A]. Proceedingsof the 1995 International Joint Conference on Artificial Intelligence [C], Montreal,Canada, 1995: 1652-1659.
[41] Lumelsky V, Stepanov A. Path Planning Strategies for a Point Mobile AutomationMoving Amongst Unknown Obstacles of Arbitrary Shape[J]. Algorithmic, 1987,3(4): 403-430.
[42] O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots [J].International Journal of Robotic Research, 1986, 5(1): 90-98.
[43] Sato K. Deadlock-free motion planning using Laplace potential field [J]. AdvancedRobotics, 1993, 7(5): 449-461.
[44] Min Gyu Park, Jae Hyun Jeon, Min Cheol Lee. Obstacle avoidance for mobilerobots using artificial potential field approach with simulated annealing [A]. ISIE2001,Pusan, Korea, 2001: 1530-1535.
[45]庄晓东,孟庆春等.复杂环境中基于人工势场优化算法的最有路径规划[J].机器人, 2003, 25(6): 531-535.
[46] Viorel Stoian, Mircea Ivanescu, Elena Stoian, et al. Using Artificial Potential FieldMethods and Fuzzy Logic for Mobile Robot Control [A]. EPE-PEMC 2006,Portoroz, Slovenia, 2006: 385-389.
[47]况菲,王耀南.基于混合人工势场-遗传算法的移动机器人路径规划仿真研究[J].系统仿真学报, 2006, 18(3): 774-777.
[48] Bagchi A., Hatwal H. Fuzzy logic based techniques for motion planning of a robotmanipulator amongst unknown moving obstacles [J]. Robotica, 1992, 10(6):563-573.
[49]庄小东,孟庆春等.动态环境中基于模糊概念的机器人路径搜索方法[J].机器人, 2001, 23(5): 397-399.
[50]张文志,吕恬生.基于改进遗传算法和模糊逻辑控制的移动机器人导航[J].机器人, 2003, 25(1): l-6.
[51] Hopfield J J, Tank D W. Neural Computation of Decisions in OptimizationProblem[J]. Biological Cybernetics, 1985, 52(1): 141-152.
[52]耿兆丰,吴永敢.基于神经网络算法的路径规划[A]. 1992年中国控制与决策学术年会论文集[C],哈尔滨, 1992: 261-265.
[53]孙增圻.智能控制理论与技术[M].北京:清华大学出版社, 1997.
[54] Fierro R, Lewis F L. Control of a Nonholonomic Mobile Robot Using NeuralNetworks[J]. IEEE Trascation on Neural Networks, 1998, 9(4): 589-600.
[55] Payeur P., Le-Huy H., Gosselin C. Robot Path Planning Using Neural Networksand Fuzzy Logic[A].Proceedings of IECON '94[C], Bologna, Italy, 1994, vol.2:800-805.
[56] Meijuan Gao, Jingwen Tian. Path Planning for Mobile Robot Based on ImprovedSimulated Annealing Artificial Neural Network[A]. Proceedings of ICNC 2007,vol.3 [C], Haikou, China, 2007: 8-12.
[57] J.H.Holland. Adaptation in Natural and Artificial Systems[M]. The Unversity ofMichigan Press, 1975.
[58] Gerke M. Genetic Path Planning for Mobile Robots [A]. Proceedings of Americancontrol conference[C]. San Diego, CA, USA, 1999: 596-601.
[59]刘雁飞,裘聿皇.基于两层编码遗传算法的机器人路径规划[J].控制理论与应用, 2000, 17(3): 429-432.
[60]陈刚.复杂环境下路径规划问题的遗传路径规划方法[J].机器人, 2001, 23(1):40-45.
[61]李庆中,顾伟康等.基于遗传算法的移动机器人动态避障路径规划方法[J].模式识别与人工智能, 2002, 15(2): 161-165.
[62]周明,孙树栋,彭炎午.基于遗传模拟退火算法的机器人路径规划[J].航空学报, 1998, 19(1): 118-120.
[63] S.Kirkpatrick. Optimization by Simulated Annealing[J]. Science, 1983, 220(4598):671-680.
[64] R. Storn, K. Price. Differential evolution– A Simple and Efficient Heuristic forGlobal optimization over continuous spaces[J]. Journal of Global Optimization,1997, 11(4): 341-359.
[65] Helder Santos, Jose Mendes, et al. Path Planning Optimization Using theDifferential Evolution Algorithm[J]. Robotica, 2003, 43(3): 382-390.
[66] Zixing Cai, Zhihong Peng. Cooperative Coevolutionary Adaptive GeneticAlgorithm in Path Planning of Cooperative Multi-Mobile Robot Systems[J].Journal of Intelligent and Robotic Systems, 2002, 33(1): 61-71.
[67] Jayasree Chakrabortya, Amit Konara, et al. Cooperative multi-robot path planningusing differential evolution[J]. Journal of Intelligent & Fuzzy Systems, 2009, 20(1):13-27.
[68] M. Dorigo, V. Maniezzo, A. Colorni. The ant system: optimization by a colony ofcooperating agent[J]. IEEE Transactions on Systems.Man and Cybernetics-Part B.1996. 26(1): 1-13.
[69]金飞虎,洪炳熔,高庆吉.基于蚁群算法的自由飞行空间机器人路径规划[J].机器人, 2002, 24(6): 526-529.
[70] K. Gopalakrishnan, S. Ramakrishnan. Optimal Path Planning of Mobile Robotwith Multiple Targets Using Ant Colony Optimization. Smart Engineering Systems,New York, 2006: 25-30.
[71] J. Kennedy, R. Eberhart. Particle swarm optimization[A]. Proceeding of the IEEEInternational Conference on Neural Networks, NewYork, NY, USA: IEEE, 1995:1942-1948.
[72] R. C. Eberhart, Y. Shi. Comparing inertia weights and constriction factors inParticle Swarm Optimization[A]. Proceedings of the Congress on EvolutionaryComputating[C]. San Diego, CA: IEEE, 2000: 84-88.
[73]秦元庆,孙德宝,李宁等.基于粒子群算法的移动机器人路径规划[J].机器人,2004, 26(3): 222-225.
[74] Yuan-qing Qin, De-bao Sun, et al. Path planning for mobile robot using the ParticleSwarm Optimization with mutation operator[A]. Proceedings of the ThirdInternational Conference on Machine Learning and Cybemetics[C], Shanghai,2004: 2473-2478.
[75]孙波,陈卫东,席玉庚.基于粒子群优化算法的移动机器人全局路径规划[J].控制与决策, 2005, 20(9): 1052-1055.
[76] Kavraki L, Latombe J. Randomized preprocessing of configuration space for fastpath planning[A]. Proceedings of the 1994 IEEE International Conference onRobotics and Automation[C], San Diego, USA: IEEE Computer Society, 1994:2138-2139.
[77] LaValle S M. Rapidly-exploring random trees: a new tool for path planning [R].Technical Report TR98-11, Computer Science Dept, Iowa State University, 1998.
[78]成伟明,唐振民等.移动机器人路径规划中的图方法应用综述[J].工程图学学报, 2008, 29(4): 6-14.
[79]石鸿雁,孙昌志.一种基于混沌优化算法的机器人路径规划方法[J].机器人,2005, 27(2): 152-157.
[80] Zeng Dehuai, Xu Gang, et al. Artificial Immune Algorithm based robotobstacle-avoiding path planning[A]. Proceedings of the IEEE InternationalConference on Automation and Logistics[C], Qingdao, China, 2008: 798–803.
[81] E. Masehian, M. R. Amin-Naseri. Sensor-Based Robot Motion Planning - A TabuSearch Approach[J]. Robotics & Automation Magazine, 2008, 15(2): 48–57.
[82]王仲民,岳宏,刘继岩.基于改进模拟退火算法的移动机器人路径规划[J].计算机工程与应用, 2005,41(19): 59-60.
[83] Chunxue Shi,Yingyong Bu Ziguang Li. Path Planning for Deep Sea Mining RobotBased on ACO-PSO Hybrid Algorithm[J]. Proceedings of 2008 InternationalConference on Intelligent Computation Technology and Automation[C], Changsha:IEEE Computer Society, 2008: 125-129.
[84]刘华军,杨静宇,陆建峰等.移动机器人运动规划研究综述[J].中国工程科学,2006, 8(1): 85-94.
[85] Goldberg K. Completeness in robot motion planning[A]. Proceedings of theworkshop on Algorithmic foundations of robotics [C], San Francisco, CA, 1994:419-429.
[1] Sung-Wook Park, Jung-Han Kim, et al. Development of a multiagent system forrobot soccer game[A]. Proceedings of IEEE International Conference on Roboticsand Automation[C], Albuquerque, New Mexico, 1997: 626-631.
[2]方帅,胡英,徐心和.集控式足球机器人视觉子系统的关键技术[J].东北大学学报(自然科学版), 2003, 24(11): 1029-1032.
[3] D. Marr. Vision[M]. San Francisco: W.H. Freeman and Company, 1982.
[4] Kuk-Hyun Han, Kang-Hee Lee, Choon-Kyoung Moon, et al. Robot soccer systemof SOTY 5 for middle league MiroSot[A]. Proceeding of FIRA Robots WorldCongress 2002[C], Seoul: Kaist Press, 2002: 632-635.
[5]胡英,赵姝颖.色标设计与辨识算法研究[J].中国图像图形学报, 2002, 7(12):1291-1295.
[6] HE Chao, XIONG Rong, DAI Lian-kui. Fast Segmentation and Identification inVision-system for Robots[A]. Proceedings of the 4th World Congress onIntelligent Control and Automation[C], Shanghai, P.R.China, 2002: 532-536.
[7]陈凤东,洪炳熔,朱莹.基于HIS颜色空间的多机器人识别研究[J].哈尔滨工业大学学报, 2004, 36(7): 928-930.
[8] F. H. Borsato, F. C. Flores. A Real Time Method to Object Detection TrackingApplied to Robot-Soccer[A]. Proceedings of the 2004 IEEE Conference onCybernetics and Intelligent Systems[C], Singapore: IEEE, 2004: 174-178.
[9] Jun Zhou, Hong-shuang Zhang, Yong Chen. Design of Vision System andRecognition Algorithm in Mirosot[A]. Proceedings of the 2004 IEEE Conferenceon Cybernetics and Intelligent Systems[C], Singapore, 2004: 153-157.
[10] James Bruce, Tucker Balch, Manuela Veloso. Fast and Inexpensive Color imageSegmentation for interactive Robots[A]. Proceedings 2000 IEEE InternationalConference on Intelligent Robots and System[C], Takamatsu, Japan, 2000:2061-2066
[11]薛方正,陈绍军,李祖枢.一种快速位姿检测算法[J].计算机科学, 2007,34(12): 246-247.
[12]彭强,江浩.大场地足球机器人视觉子系统及其识别算法[J].西南交通大学学报, 2005, 40(2): 168-172.
[13] Chris Messom, Nazeer Ahmed, Gourab Sen Gupta. Calibration and auto-tuning ofa realtime RLE vision system[A]. FIRA Robot World Congress 2003[C], Vienna,Austria, 2003.
[14]吴宪祥,郭宝龙. Fira中基于较短轴的色标分块及朝向角辨识算法[J].光电子·激光, 2006, 17(11): 1361-1365.
[15]田国会,尹建芹等.足球机器人视觉系统及其关键问题[J].山东工业大学学报,2002, 32(1): 87-91.
[16]周军,李奎,张伟等.基于角度补偿的色标辨识算法的设计[J].哈尔滨工业大学学报, 2004, 36(7): 969-971.
[17] WANG Yu, WANG Yong, ZU Chun-shan, et al. Design and Modeling ofOmni-Directional Vision System of the Autonomous Soccer Robot[J]. Journal ofOptoelectronics·Laser, 2006, 17(10): 1196-1200.
[1] C. Kuglin, D. Hines. The phase correlation image alignment method. Conferenceon Cybernetics and Society[C]. New York: IEEE, 1975: 163-165.
[2] WU Xian-xiang, GUO Bao-long. FFT-based orientation identification algorithm infira [J]. Journal of Optoelectronics·Laser, 2008, 19(5): 652-655.
[3]吴宪祥,郭宝龙,王娟.一种改进的序列图像自动排序算法[J].光电子·激光,2009, 20(8): 1110-1113.
[4] R. Song, J. Szymanski. Auto-sorting scheme for image ordering applications inimage mosaicing[J]. Electronics Letters, 2008, 44(13): 798-799.
[5]吴宪祥,郭宝龙,王娟.基于相位相关的柱面全景图像自动拼接算法[J].光学学报, 2009, 29(7): 1824-1829.
[6] S. Reddy and B.N. Chatterj. An FFT-based technique for translation, rotation, andscale-invariant image registration[J]. IEEE Trans. Image Process, 1996, 3(8):1266-1270.
[7] Woberg George, Zokai Siavash. Robust Image Registration Using Log-polarTransform[A]. Proceedings of the IEEE International Conference on ImageProcessing[C]. Vancouver, Canada: IEEE, 2000: 493-496.
[8] Y. Keller, A. Averbuch, M. Israeli. Pseudopolar-Based estimation of largetranslations, rotations, and scalings in images[J], IEEE Trans. Image Processing,2005, 14(1): 12-22.
[9] Liu Han-zhou, Guo Bao-long, Feng Zong-zhe. Pesudo-log-polar Fourier Transformfor Image Registration[J]. IEEE Signal Processing Letters, 2006, 13(1): 17-20.
[10] J. Dongarra, F. Sullivan. Guest Editors Introduction to the Top 10 Algorithms[J],Computing in Science & Engineering, 2000, 2(1): 22-23.
[11] L. Briggs, E. Henson. The DFT: An Owner’s Manual for the Discrete FourierTransform[M], Philadelphia: SIAM, 1995.
[12] A. Averbuch, R.R. Coifman, D.L. Donoho, et al. Accurate and fast Polar Fouriertransform[J]. Applied and Computational Harmonic Analysis, 2006, 21(2):145-167.
[13] Karsten Fourmont. Non-equispaced fast Fourier transforms with applications totomography [J]. Journal of Fourier Analysis and Applications, 2003, 9(5):431-450.
[14] G. Beylkin. On the fast Fourier transform of functions with singularities[J].Applied and Computational Harmonic Analysis, 1995, 2(4): 363-381.
[15] A. Dutt, V. Rokhlin. Fast Fourier transforms for nonequispaced data[J]. SIAMJournal on Scientific Computing, 1993, 14(6): 1368-1393.
[16] A.F. Ware. Fast approximate Fourier transform for irregularly spaced data, SIAMReview, 1998, 40(4): 838-856.
[17] J.A. Fessler, B.P. Sutton. Nonuniform fast Fourier transforms using min–maxinterpolation[J]. IEEE Transactions on Signal Processing, 2003, 51(2): 560-574.
[18] L. Greengard, J.Y. Lee. Accelerating the nonuniform fast Fourier transform[J].SIAM Review, 2004, 46(3): 443-454.
[19] M. Fenn, S. Kunis, D. Potts. On the computation of the polar FFT[J]. Applied andComputational Harmonic Analysis, 2007, 22(2): 257-263.
[20] D. Potts, M. Tasche. Numerical stability of nonequispaced fast Fouriertransforms[J]. Journal of Computational and Applied Mathematics, 2008, 222(2):655-674.
[21] R.M. Mersereau, A.V. Oppenheim. Digital reconstruction of multidimensionalsignals from their projections[J]. Proceedings of the IEEE, 1974, 62(10):1319-1338.
[22] P.R. Edholm, G.T. Herman. Linograms in image reconstruction from projections[J].IEEE Transactions on Medical Imaging, 1987, 6(4): 301-307.
[23] W. Lawton. A new Polar Fourier-transform for computer-aided tomography andspotlight synthetic aperture radar[J]. IEEE Transactions on Acoustics, Speech, andSignal Processing, 1988, 36(6): 931-933.
[24] A. Averbuch, Y. Shkolnisky. 3D Fourier based discrete Radon transform[J].Applied and Computational Harmonic Analysis, 2003, 15(1): 33-69.
[25] L.R. Rabiner, R.W. Schafer, C.M. Rader. The Chirp Z-transform algorithm and itsapplication[J]. Bell System Technical Journal, 1969, 48(5): 1249-1292.
[26] D.H. Bailey, P.N. Swarztrauber. The fractional Fourier transform andapplications[J]. SIAM Review, 1991, 33(3): 389-404.
[27] Ofir Harari. A new nearly-Polar FFT and analysis of Fourier-Radon relations indiscrete spaces [D]. Master dissertation, Ben-Gurion University of the Negev,2007.
[28] Xian-xiang Wu, Bao-long Guo, Juan Wang. Octa-Log-Polar Fourier Transform forImage Registration. Proceedings of the 2009 Fifth International Conference onInformation Assurance and Security, Xi’an, China: IEEE, 2009: vol.1: 601-604.
[29]吴宪祥,郭宝龙.基于傅里叶变换的Fira机器人朝向角辨识算法[J].光电子·激光, 2008, 19(5): 652-655.
[30] Xian-xiang Wu, Bao-long Guo. FFT-Based Orientation Recognition Algorithm inMiroSot[A]. Proceedings of the 2008 Eighth International Conference onIntelligent Systems Design and Applications[C], Kaohsiung: IEEE, 2008, vol.2:478-481.
[1]陈宝林.最优化理论与算法(第2版)[M].北京:清华大学出版社, 2005.
[2] Singiresu S. Rao. Engineering Optimization: Theory and Practice (Fourth Edition)[M], Hoboken, New Jersey: Wiley, 2009.
[3] J. J. Hopfield, D.W. Tank.“Neural”computation of decisions in optimizationproblems[J]. Biological Cyberntics, 1985, 52(3): 141-152.
[4] John H. Holland. Adaptation in Natural and Artificial Systems[M]. Ann Abor, MI:The University of Michigan Press, 1975.
[5] D. Dasgupta. Advances in artificial immune systems[J]. IEEE ComputationalIntelligence Maganize, 2006, 1(4): 40-49.
[6] M. Dorigo, V. Maniezzo, A. Colorni. The ant system: optimization by a colony ofcooperating agents, IEEE Transactions On Systems, Man and CyberneticsPartB ,1996, 26(1): 29-41.
[7] S. Kirkpatrick, C. D. Gelatt, et al. Optimization by simulated annealing[J]. Science,1983, 220(4598): 671-680.
[8] S. Kirkpatrick. Optimization by simulated annealing: quantitative studies [J].Journal of Statistical Physics, 1984, 34(5): 975-986.
[9] J. Kennedy, R. C. Eberhart. Particle Swarm Optimization[A]. Proceedings of IEEEInternational Conference on Neural Networks, IV[C], IEEE Service Center,Piscataway, NJ, 1995: 1942-1948.
[10] F. Glover. Tabu Search-Part I [J]. ORSA Journal on Computing, 1989, 1(3):190-206.
[11] F. Glover. Tabu Search-Part II [J]. ORSA Journal on Computing, 1990, 2(1): 4-32.
[12]李兵,蒋慰孙.混沌优化方法及其应用[J].控制理论与应用, 1997, 14(3):285-288.
[13] E. Bonabeau, M. Dorigo, G. Theraulaz. Swarm Intelligence-from Natural toArtificial System[M]. Oxford: Oxford University Press, 1999.
[14] J. Kennedy, R. C. Eberhart. Swarm Intelligence[M]. USA: Academic Press, 2001.
[15] M. Millonas. Swarms, phase transitions, and collective intelligence[A].Computationa Intelligence: A Dynamic System Perspective, IEEE Press,Piscataway, NJ, 1994: 137-151.
[16] C. W. Reynold. Flocks, herds, and schools: a distributed behavioral model[J].Computer Graphics, 1987, 21(4): 25-34.
[17] Edward O. Wilson. Sociobiology: The New Synthesis. Cambridge, Mass.: BelknapPress, 1975.
[18] Y. Shi, R.C. Eberhart. A modified particle swarm Optimizer[A]. Proceedings of theIEEE Congress on Evolutionary Coputation[C], Piscataway, NJ: IEEE Press, 1998:69-73.
[19] R. C. Eberhart. Y. Shi. Comparing inertia weights and constriction factors inParticle Swarm Optimization[A]. Proceedings of the Congress on EvolutionaryComputation[C]. San Diego, CA: IEEE, 2000: 84-88.
[20] M. Clerc , J. Kennedy. The particle swarm-explosion, stability and convergence ina multidimensional complex space [J]. IEEE Transactions on EvolutionaryComputation, 2002, 6(1): 58-73.
[21] A. Carlisle, G. Dozier. Adapting particle swarm optimization to dynamicenvironments[A]. Proceedings of International Conference on ArtificialIntelligence [C], Las Vegas, Nevada, USA, 2000: 429-434.
[22] T. M. Blackwell. Particle swarms and population diversity[J]. Soft Computing- AFusion of Foundations, Methodologies and Applications, 2005, 9(11): 793-802.
[23] T. Blackwell, J. Branke. Multiswarms, Exclusion, and Anti-Convergence inDynamic Environments[J]. IEEE Transactions on Evolutionary Computation, 2006,10(4): 459-472.
[24] T. M. Blackwell, P. J. Bentley. Dynamic search with charged swarms[A].Proceedings of the Genetic and Evolutionary Computation Conference 2002[C],New York, USA: Morgan Kaufmann, 2002: 19-26.
[25] Liping Zhang, Huanjun Yu, Shangxu Hu. A new approach to improve ParticleSwarm Optimization. Lecture Notes in Computer Science (LNCS) No.2723:Proceedings of the Genetic and Evolutionary Computation Conference2003(GECCO 2003), Chicago, IL, USA, 2003: 134-142.
[26]高浩,冷文浩,须文波.一种全局收敛的PSO算法及其收敛分析[J].控制与决策. 2009, 24(2): 196-201.
[27] X. Hu, R. C. Eberhart. Adaptive Particle Swarm Optimization: Detection andResponse to Dynamic Systems. Proceedings of IEEE Congress on EvolutionaryComputation(CEC2002), Honolulu, Hawaii USA: IEEE, 2002: 1666-1670.
[28]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报, 2004, 32(3):416-420.
[29]高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学, 2004, 31(8): 13-15.
[30] J. Kennedy. Small worlds and mega-minds:effects of neighborhood topology onparticle swarm performance[A]. Proceedings of the 1999 IEEE Congress onEvolutionary Computation [C], Piscataway, NJ: IEEE Press, 1999: 1931-1938.
[31] J. Kennedy, R. Mendes. Population Structure and Particle Swarm Performance [A].Proceedings of the 2002 World Congress on Computational Intelligence[C],Hawaii, USA: IEEE Press, 2002: 1671-1676.
[32] R. Mendes, J. Kennedy, J. Neves. The Fully Informed Particle Swarm: Simpler,Maybe Better [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3):204-210.
[33] J. Branke. Memory enhanced evolutionary algorithms for changing optimizationproblems[A]. Proceedings of the 1999 Congress on Evolutionary Computation[C], Washington D.C., USA: IEEE Press, 1999, vol.3: 1875-1882.
[34] Y. Yan, B. Guo. Particle Swarm Optimization Inspired by r- and K-Selection inEcology[A]. Proceedings of 2008 IEEE Congress on Evolutionary Computation(CEC 2008)[C], Hong Kong, China: IEEE Press, 2008: 1117-1123.
[35]李博,杨持等.生态学[M].北京:高等教育出版社, 2000.
[36]孙濡泳,李庆芬,牛翠娟等.基础生态学[M].北京:高等教育出版社, 2002.
[37]达尔文著,周建人,叶笃庄,方宗熙译.物种起源[M].北京:商务印书馆,1995.
[38] Karin Kiontke, Antoine Barrière, Irina Kolotuev et.al. Trends, Stasis, and Drift inthe Evolution of Nematode Vulva Development [J]. Current Biology, 2007, 17(22):1925-1937.
[39]张琴. Lotka-Volterra模型的若干问题[D].吉林大学硕士学位论文, 2006.
[40]高鹰,姚振坚,谢胜利.基于种群密度的粒子群优化算法[J].系统工程与电子技术, 2006, 28(6): 922-924.
[41]尚玉昌,蔡晓明.普通生态学[M] .北京:北京大学出版社, 1996.
[42]吴宪祥,郭宝龙,王娟.基于Lotka-Volterra模型的双群协同竞争粒子群优化算法[J].控制与决策, 2009(已录用).
[43]闫允一.粒子群优化及其在图像处理中的应用研究[D].西安电子科技大学博士学位论文, 2008.
[1] J.A.P. Kjellander. Smoothing of cubic parametric splines [J]. Computer-AidedDesign, 1983, 15(3): 175-179.
[2]朱安风.基于双曲函数的H-Bézier和Ferguson曲线[D].合肥工业大学硕士学位论文, 2007: 22-29.
[3] James Ferguson. Multivariable Curve Interpolation [J]. Journal of the Associationfor Computing Machinery, 1964, 22(2): 221-228.
[4] J. Ye, R. Qu. Fairing of parametric cubic splines [J]. Mathematical and ComputerModelling. 1999, 30(5): 121-131.
[5]刘华军,杨静宇,陆建峰,等.移动机器人运动规划研究综述[J].中国工程科学,2006, 8(1): 85-94.
[6]马兆青,袁曾任.基于栅格方法的移动机器人实时导航和避障[J].机器人,1996, 18(6): 344-348.
[7] Keron Y, Borenstein J . Potential field methods and their inherent limitations formobile robot navigation [A] . Proceedings of the International Conference onRobotics and Automation [C]. California, 1991. 1398-1404.
[8]秦元庆,孙德宝,李宁等.基于粒子群算法的移动机器人路径规划[J].机器人,2004, 26(3): 222-225.
[9] Lumelsky V, Stepanov A. Path Planning Strategies for a Point Mobile AutomationMoving Amongst Unknown Obstacles of Arbitrary Shape[J]. Algorithmic, 1987, 3(4): 403-430.
[10] Marcelo Kallmann. Path Planning in Triangulation [A]. Proceedings of theworkshop on reasoning, representation, and learning in computer games[C].Edinburgh, Scotland, 2005: 49-54.
[11]梅昊,田彦涛,祖丽楠.动态环境下机器人路径规划的混合蚁群算法[J].吉林大学学报(信息科学版), 2006, 24(2) :148-152.
[12] Yang Linquan, Luo Zhongwen, Tang Zhonghua, et al. Path Planning Algorithm forMobile Robot Obstacle Avoidance Adopting Bezier Curve Based on GeneticAlgorithm[A]. Proceedings of 2008 Chinese Control and Decision Conference[C].Yantai: IEEE, 2008:3286-3289.
[13] K.G. Jollyb, R. Sreerama Kumar, R. Vijayakumar. A Bezier curve based pathplanning in a multi-agent robot soccer system without violating the accelerationlimits[J]. Robotics and Autonomous Systems, 2009, 57(1): 23-33.
[14] R. C. Eberhart. Y. Shi. Comparing inertia weights and constriction factors inParticle Swarm Optimization[A]. Proceedings of the Congress on EvolutionaryComputation[C]. San Diego, CA: IEEE, 2000: 84-88.
[15] M. Clerc, J. Kennedy. The particle swarm-explosion, stability and convergence ina multidimensional complex space [J]. IEEE Transactions on EvolutionaryComputation, 2002, 6(1): 58-73.
[16] Xin Chen, Yangmin Li. Smooth Path Planning of a Mobile Robot Using StochasticParticle Swarm Optimization[A]. Proceedings of the 2006 IEEE InternationalConference on Mechatronics and Automation[C]. Luoyang: IEEE, 2006:1722-1727.
[17] Y.Q. Qin, D.B. Sun, M. Li, et al. Path Planning for Mobile Robot Using theParticle Swarm Optimization with Mutation Operator [A]. Proceedings ofInternational Conference on Machine Learning and Cybernetics, vol. 4 [C], 2004:2473-2478.
[18] Li Wang, Yushu Liu, Hongbin Deng, et al. Obstacle-avoidance Path Planning forSoccer Robots Using Particle Swarm Optimization[A]. Proceedings of the 2006IEEE International Conference on Robotics and Biomimetics[C], Kunming: IEEE,2006: 1233-1238.
[19]吴宪祥,郭宝龙,王娟.基于粒子群三次样条优化的移动机器人路径规划算法[J].机器人, 2009, 31(6): 556-560.
[20] M. Saska, M. Macas, L. Preucil. Robot Path Planning using Particle SwarmOptimization of Ferguson Splines[A]. ETFA 2006 Proceedings [C]. Piscataway:IEEE, 2006: 833-839.
[21]吴宪祥,郭宝龙,王娟.基于Lotka-Volterra模型的双群协同竞争粒子群优化算法[J].控制与决策, 2009(已录用).