固体火箭发动机壳体内壁绝热层打磨机器人关键问题的研究
|
摘要
固体火箭发动机是航天工程中重要的动力装备,粘贴在燃烧室(即发动机壳体)内壁的绝热层,对燃烧室壳体起到隔热、防止燃烧产物冲刷等作用。目前,我国在绝热层的打磨中仍采用手工操作方式。手工打磨,不仅效率低、打磨时周围充满粉尘,工作环境极其恶劣,并且难以保证打磨的均匀性。而打磨厚度过多或过少都会使绝热层以至整个发动机壳体的报废,造成极大的经济损失,这种制造方式已不能适应航天工业当今和未来发展的需要。
研制先进的“绝热层自动打磨装备”是航天工业的发展趋势,也是所在研究室与中国航天科工集团某厂合作的实际项目“固体火箭发动机壳体内壁绝热层打磨机器人”。基于以上实际需要,本文对绝热层打磨机器人研制中的关键技术问题进行了详细的分析与论述,并给出有效的解决方案与方法,主要研究内容与成果如下：
(1)固体火箭发动机绝热层的制造,涉及军事、航天等敏感领域,国际上相关报道很少,而国内该领域的自动化生产水平低下,没有前例可以参照。同时由于绝热层结构尺寸与内壁精确打磨要求的特殊性,对打磨装置本身的形状尺寸及其在操作过程中的运动都有特殊地要求与限制,使其不同于传统的零部件外表面的打磨。因此,在对绝热层结构尺寸及其打磨技术要求进行详细地分析、并借鉴应用于其它领域的自动化打磨装置的设计的基础上,设计了一个安装有专用打磨装置的特种绝热层内壁打磨机器人,并从机械结构、打磨方式、功能模块、性能指标等方面给出了整体设计方案。
(2)根据建立的打磨机器人机械本体综合优化模型的特点,采用基于改进编码方式和加入小范围竞争的遗传算法进行优化求解。优化模型中的优化变量皆为两位或三位的正整数,所以采用正整数十进制组合作为基因表对染色体进行编码,这样对基因的遗传操作实质上就是对优化变量的遗传操作,既利用了遗传算法的优化机制,又符合常规的对优化变量进行搜索的优化设计思想,同时也减少了遗传操作中染色体的个数,提高了优化的效率。而小范围竞争可以避免由于大量的同一父代产生的个体在下代中进行遗传操作而陷入局部极限。优化结果表明,在GA算法中采用正整数十进制组合编码方式和小生竞争机制,既可以明显地减少搜索代数,又避免了由于“近亲繁殖”而在某一数值附近重复搜索,增强了全局搜索能力。通过对打磨机器人机械本体的综合优化设计,得到最佳的设计方案,指导机器人机械本体的设计与制造。
(3)针对传统的应用于自由操作空间的机器人冗余度干涉规避方法,无法解决“细长”形壳体内壁绝热层打磨机器人各关节杆件间、关节杆件与内壁间的干涉规避问题,本文提出一种在受限操作空间中无冗余干涉避障方法。该方法在不增加打磨机器人自由度的前提下,只改变末端执行器(即打磨头)的设计形状,然后通过数学模型判别在打磨轨迹上各关节杆件间、关节杆间与壳体内壁问是否发生干涉,动态地调整打磨角,从而改变机器人各杆件的位置与姿态,实现干涉和碰撞的规避。无冗余干涉避障方法在最低程度改变原有机械本体的前提下,通过对机器人各关节杆件在操作过程中的运动形态的优化控制实现了干涉规避,为机器人的干涉避障提供了新的方法与思路。
(4)在机器人轨迹规划中引入数控S形加减速规划方法,并通过分析规划方法中各参数之间的关系,给出求解S形加减速算式的约束条件,简便了求解过程。同时,在对比了等切线长、等弦长和等弧长三种时间分割法的基础上,选用弓高误差最小的等弧长时间分割法对椭圆打磨轨迹段进行插补计算,把经由前S形加减速算规划每一运动控制周期的位移量,精确的、实时的转换为相应的打磨点坐标。仿真数据说明,打磨机器人的运动轨迹规划与插补方法,精度高、速度快、平稳性好,为实现壳体内壁绝热层的精确打磨提供了必要的前提条件。
(5)由于绝热层在局部为几何参数未知的非规则平滑曲面,本文在理想运动轨迹规划和经典控制方法的基础上,采用基于神经网络预测参考轨迹的阻抗控制系统进行控制补偿,使打磨机器人具备柔顺控制的能力。通过推导,证明了以位置传感器和力传感器的采集信号作为输入、以非规则表面预测轨迹参数和环境预测刚度为输出的4层神经网络感知器预测参考轨迹模型的可行性。另外,针对阻抗控制系统中的实际检测接触力Pm很难直接获得,采用采集各伺服电机转矩值、经过换算得到末端执行器与外部环境的接触力的方法,来替代直接采用力传感器反馈末端执行器接触力信号；对期望接触力Pd,则设计了一种实验方法得到打磨力和在其作用下的被打磨厚度,预估出期望的打磨力范围,再根据神经网络预测参考轨迹的阻抗控制系统在线进行局部的微量调整,以满足打磨要求。实验数据表明,打磨厚度在打磨要求范围内,保证了打磨的质量与安全。
(6)在打磨过程中,机械本体的大部分在壳体内部工作,有必要建立机械本体各杆件运动的三维图形在线监测系统。因此,本文利用Solid Edge三维建模软件及其二次开发技术,实现机器人参数化建模；在此基础上,再通过Solid Edge公开地装配体和零部件操作中的五个驱动函数,用在线位置反馈数据驱动模型中各关节杆件运动,简便地实现了打磨机器人各关节杆件运动形态的在线三维监测,以便快速准确地作出判断与决策。
Solid rocket motor is the most important space power equipment. The insulation pasted in the combustion chamber (namely, the engine shell) inner-wall plays an important role in thermal insulation and avoiding washing to shell by combustion products. Presently, the domestic production is still by manual method. This manufacturing not only is inefficient, labor-intensie, extremely poor working conditions, but also is not able to ensure uniformity of grinding. The thickness of grinding too much or too little will lead to scrap the insulation as well as the whole engine. So this manufacturing has not meet today's aerospace industry and future development needs.
Development of advanced automatic grinding equipment insulation is the development trend of the space industry and also one practical co-operation projects between my research department and a factory of China Aerospace Science and Industry Group. Consequently, in this dissertation, the key technical issues of rocket engines inner-wall grinding robot have been made a detailed analysis and discussion. Moreover, according with the key problems, the effective solutions and their theoretical basis and technical support are given. The main research content and results are as follows:
(1) The manufacturing of advanced automatic rocket engines inner-wall grinding equipment involves in military, aerospace and other sensitive areas, so there are few relevant reports international. And the automation level of domestic production is backward. Consequently, there is no precedent for reference. Moreover, due to the specificity requirements of grinding and the structure of insulation, they all limit the shape and size of robot mechanical structure and the movement patterns in grinding process. Hence, the manufacturing of advanced automatic grinding is different from the traditional outer surface. Hence, reference the design of grinding automation applied to other areas, the overall function and structure of grinding robot is designed from the aspects of the polished manner, functional modules and performance indicators.
(2) Based on the characters of integrated optimal design of grinding robot mechanical structure, the improved encoding method and involing competition in small scope in GA is applied to optimize. Because the optimization model has six variables and all of them are positive integers, combination of ten numbers is used as chromosome table to encode genes. Consequently, the genetic manipulation of genes is essentially the genetic manipulation of optimal variables. This method not only uses the optimization mechanism of genetic algorithm, but also accords with the conventional optimization design idea. Moreover, combination encoding also reduces the numbers of chromosome so that it can also improve the efficiency of optimization search. Moreover, the competition in small scope is able to avoidng the individuals generated by the same parental type great carrid on genetic manipulation.
(3) Traditional robot collision avoidance method applied to free operation space is carried out by adding robot DOF(Degree of Freedom). However, this method isn't suitable to limited operation space such as a kind of slightness solid-propellant rocket engines inner-wall. Hence, this dissertation presents a new robot method with non-redundant DOF for limited operation space collision avoidance. Without adding robot DOF, this method judges collision situation in grinding trajectory by mathematical model, then adjusts dynamically the grinding interface of the robot end-effector. So robot bodies'poses are transformed to avoid collisions between the different robot bodies or between robot bodies and inner-wall. Moreover, kinematics optimization and simulation were carried on by setting the smoothness of joints'velocity variety as optimization objective and discriminant of collision as constraints. On the premises of minimum changing of original, this method use the optimal control way to collision avoidance.
(4) Referring to the pre-interpolation S-shape acceleration/deceleration and smooth transfer in CNC, the grinding robot trajectory planning is carried on. The trajectory planning is able to avoid impact, overshoot and oscillation in the start and stop processes of robot basic. And through the analysis of parametric relationship, the constraints of S-shape acceleration/deceleration is presented to simplify the solution. Moreover, the heads of solid-propellant rocket engines body are ellipsoid thin-wall structure so the ellipse trajectory curve is not approximated by general linear or circular interpolation method. Otherwise, it will create a big bow high error. In view of the defects of above methods of interpolation, based on the contrast of three different ellipse interpolation algorithm, the equal arc length time-sharing interpolation algorithm is used to transform the displacement planned by S-shape acceleration/deceleration. The simulation results prove the inner-wall grinding robot's trajectory planning and interpolation algorithm with high precision, speed and smoothness. All of these provide the necessary precondition for accuracy grinding of missile inner-wall grinding.
(5) Because the insulation exist some local convex and cupped, this dissertation presents a impedance control system with multilayer perception NN-based predicting reference trajectory to compensate the ideal trajectory and control. Through formula deduction, the feasibility of NN-based predicting trajectory is proved with the NN sample inputs set as the actual contact trajectory points and contact forces, and the NN sample outputs set as the environment predictive trajectory points, stiffness and tangential angle.
In addition, the actual force Pm and expected force Pd by the predictive trajectory based on neural network impedance reference control system are difficult to be directly access. Hence, Pm is substituted by the acquisition of the servo motor torque, which can be converted to the contact force of the end effector. And an experimental method has been designed to get Pd. In order to meet the requirements the method predict the expected grinding force referred to the grinding force that be indirectly measured and the thickness that be polished, and adjust grinding force slightly based on neural network reference trajectory of the impedance control system. Experimental data show that the thickness of griding can meet the requirements and ensure the quality and safety of grinding.
(6) In the grinding process, the grinding robot mechanical structure is almost in the rocket engines, so it is necessary to build the three dimensions graph monitoring system on-line of grinding robot's movement. In this dissertation, Solid Edge software and its secondary development technology are used to establish the parameters three dimensions model of grinding robot. Then, through five command functions provided openly by Solid Edge software, the grinding robot's monitoring system online are build simply.
研制先进的“绝热层自动打磨装备”是航天工业的发展趋势,也是所在研究室与中国航天科工集团某厂合作的实际项目“固体火箭发动机壳体内壁绝热层打磨机器人”。基于以上实际需要,本文对绝热层打磨机器人研制中的关键技术问题进行了详细的分析与论述,并给出有效的解决方案与方法,主要研究内容与成果如下：
(1)固体火箭发动机绝热层的制造,涉及军事、航天等敏感领域,国际上相关报道很少,而国内该领域的自动化生产水平低下,没有前例可以参照。同时由于绝热层结构尺寸与内壁精确打磨要求的特殊性,对打磨装置本身的形状尺寸及其在操作过程中的运动都有特殊地要求与限制,使其不同于传统的零部件外表面的打磨。因此,在对绝热层结构尺寸及其打磨技术要求进行详细地分析、并借鉴应用于其它领域的自动化打磨装置的设计的基础上,设计了一个安装有专用打磨装置的特种绝热层内壁打磨机器人,并从机械结构、打磨方式、功能模块、性能指标等方面给出了整体设计方案。
(2)根据建立的打磨机器人机械本体综合优化模型的特点,采用基于改进编码方式和加入小范围竞争的遗传算法进行优化求解。优化模型中的优化变量皆为两位或三位的正整数,所以采用正整数十进制组合作为基因表对染色体进行编码,这样对基因的遗传操作实质上就是对优化变量的遗传操作,既利用了遗传算法的优化机制,又符合常规的对优化变量进行搜索的优化设计思想,同时也减少了遗传操作中染色体的个数,提高了优化的效率。而小范围竞争可以避免由于大量的同一父代产生的个体在下代中进行遗传操作而陷入局部极限。优化结果表明,在GA算法中采用正整数十进制组合编码方式和小生竞争机制,既可以明显地减少搜索代数,又避免了由于“近亲繁殖”而在某一数值附近重复搜索,增强了全局搜索能力。通过对打磨机器人机械本体的综合优化设计,得到最佳的设计方案,指导机器人机械本体的设计与制造。
(3)针对传统的应用于自由操作空间的机器人冗余度干涉规避方法,无法解决“细长”形壳体内壁绝热层打磨机器人各关节杆件间、关节杆件与内壁间的干涉规避问题,本文提出一种在受限操作空间中无冗余干涉避障方法。该方法在不增加打磨机器人自由度的前提下,只改变末端执行器(即打磨头)的设计形状,然后通过数学模型判别在打磨轨迹上各关节杆件间、关节杆间与壳体内壁问是否发生干涉,动态地调整打磨角,从而改变机器人各杆件的位置与姿态,实现干涉和碰撞的规避。无冗余干涉避障方法在最低程度改变原有机械本体的前提下,通过对机器人各关节杆件在操作过程中的运动形态的优化控制实现了干涉规避,为机器人的干涉避障提供了新的方法与思路。
(4)在机器人轨迹规划中引入数控S形加减速规划方法,并通过分析规划方法中各参数之间的关系,给出求解S形加减速算式的约束条件,简便了求解过程。同时,在对比了等切线长、等弦长和等弧长三种时间分割法的基础上,选用弓高误差最小的等弧长时间分割法对椭圆打磨轨迹段进行插补计算,把经由前S形加减速算规划每一运动控制周期的位移量,精确的、实时的转换为相应的打磨点坐标。仿真数据说明,打磨机器人的运动轨迹规划与插补方法,精度高、速度快、平稳性好,为实现壳体内壁绝热层的精确打磨提供了必要的前提条件。
(5)由于绝热层在局部为几何参数未知的非规则平滑曲面,本文在理想运动轨迹规划和经典控制方法的基础上,采用基于神经网络预测参考轨迹的阻抗控制系统进行控制补偿,使打磨机器人具备柔顺控制的能力。通过推导,证明了以位置传感器和力传感器的采集信号作为输入、以非规则表面预测轨迹参数和环境预测刚度为输出的4层神经网络感知器预测参考轨迹模型的可行性。另外,针对阻抗控制系统中的实际检测接触力Pm很难直接获得,采用采集各伺服电机转矩值、经过换算得到末端执行器与外部环境的接触力的方法,来替代直接采用力传感器反馈末端执行器接触力信号；对期望接触力Pd,则设计了一种实验方法得到打磨力和在其作用下的被打磨厚度,预估出期望的打磨力范围,再根据神经网络预测参考轨迹的阻抗控制系统在线进行局部的微量调整,以满足打磨要求。实验数据表明,打磨厚度在打磨要求范围内,保证了打磨的质量与安全。
(6)在打磨过程中,机械本体的大部分在壳体内部工作,有必要建立机械本体各杆件运动的三维图形在线监测系统。因此,本文利用Solid Edge三维建模软件及其二次开发技术,实现机器人参数化建模；在此基础上,再通过Solid Edge公开地装配体和零部件操作中的五个驱动函数,用在线位置反馈数据驱动模型中各关节杆件运动,简便地实现了打磨机器人各关节杆件运动形态的在线三维监测,以便快速准确地作出判断与决策。
Solid rocket motor is the most important space power equipment. The insulation pasted in the combustion chamber (namely, the engine shell) inner-wall plays an important role in thermal insulation and avoiding washing to shell by combustion products. Presently, the domestic production is still by manual method. This manufacturing not only is inefficient, labor-intensie, extremely poor working conditions, but also is not able to ensure uniformity of grinding. The thickness of grinding too much or too little will lead to scrap the insulation as well as the whole engine. So this manufacturing has not meet today's aerospace industry and future development needs.
Development of advanced automatic grinding equipment insulation is the development trend of the space industry and also one practical co-operation projects between my research department and a factory of China Aerospace Science and Industry Group. Consequently, in this dissertation, the key technical issues of rocket engines inner-wall grinding robot have been made a detailed analysis and discussion. Moreover, according with the key problems, the effective solutions and their theoretical basis and technical support are given. The main research content and results are as follows:
(1) The manufacturing of advanced automatic rocket engines inner-wall grinding equipment involves in military, aerospace and other sensitive areas, so there are few relevant reports international. And the automation level of domestic production is backward. Consequently, there is no precedent for reference. Moreover, due to the specificity requirements of grinding and the structure of insulation, they all limit the shape and size of robot mechanical structure and the movement patterns in grinding process. Hence, the manufacturing of advanced automatic grinding is different from the traditional outer surface. Hence, reference the design of grinding automation applied to other areas, the overall function and structure of grinding robot is designed from the aspects of the polished manner, functional modules and performance indicators.
(2) Based on the characters of integrated optimal design of grinding robot mechanical structure, the improved encoding method and involing competition in small scope in GA is applied to optimize. Because the optimization model has six variables and all of them are positive integers, combination of ten numbers is used as chromosome table to encode genes. Consequently, the genetic manipulation of genes is essentially the genetic manipulation of optimal variables. This method not only uses the optimization mechanism of genetic algorithm, but also accords with the conventional optimization design idea. Moreover, combination encoding also reduces the numbers of chromosome so that it can also improve the efficiency of optimization search. Moreover, the competition in small scope is able to avoidng the individuals generated by the same parental type great carrid on genetic manipulation.
(3) Traditional robot collision avoidance method applied to free operation space is carried out by adding robot DOF(Degree of Freedom). However, this method isn't suitable to limited operation space such as a kind of slightness solid-propellant rocket engines inner-wall. Hence, this dissertation presents a new robot method with non-redundant DOF for limited operation space collision avoidance. Without adding robot DOF, this method judges collision situation in grinding trajectory by mathematical model, then adjusts dynamically the grinding interface of the robot end-effector. So robot bodies'poses are transformed to avoid collisions between the different robot bodies or between robot bodies and inner-wall. Moreover, kinematics optimization and simulation were carried on by setting the smoothness of joints'velocity variety as optimization objective and discriminant of collision as constraints. On the premises of minimum changing of original, this method use the optimal control way to collision avoidance.
(4) Referring to the pre-interpolation S-shape acceleration/deceleration and smooth transfer in CNC, the grinding robot trajectory planning is carried on. The trajectory planning is able to avoid impact, overshoot and oscillation in the start and stop processes of robot basic. And through the analysis of parametric relationship, the constraints of S-shape acceleration/deceleration is presented to simplify the solution. Moreover, the heads of solid-propellant rocket engines body are ellipsoid thin-wall structure so the ellipse trajectory curve is not approximated by general linear or circular interpolation method. Otherwise, it will create a big bow high error. In view of the defects of above methods of interpolation, based on the contrast of three different ellipse interpolation algorithm, the equal arc length time-sharing interpolation algorithm is used to transform the displacement planned by S-shape acceleration/deceleration. The simulation results prove the inner-wall grinding robot's trajectory planning and interpolation algorithm with high precision, speed and smoothness. All of these provide the necessary precondition for accuracy grinding of missile inner-wall grinding.
(5) Because the insulation exist some local convex and cupped, this dissertation presents a impedance control system with multilayer perception NN-based predicting reference trajectory to compensate the ideal trajectory and control. Through formula deduction, the feasibility of NN-based predicting trajectory is proved with the NN sample inputs set as the actual contact trajectory points and contact forces, and the NN sample outputs set as the environment predictive trajectory points, stiffness and tangential angle.
In addition, the actual force Pm and expected force Pd by the predictive trajectory based on neural network impedance reference control system are difficult to be directly access. Hence, Pm is substituted by the acquisition of the servo motor torque, which can be converted to the contact force of the end effector. And an experimental method has been designed to get Pd. In order to meet the requirements the method predict the expected grinding force referred to the grinding force that be indirectly measured and the thickness that be polished, and adjust grinding force slightly based on neural network reference trajectory of the impedance control system. Experimental data show that the thickness of griding can meet the requirements and ensure the quality and safety of grinding.
(6) In the grinding process, the grinding robot mechanical structure is almost in the rocket engines, so it is necessary to build the three dimensions graph monitoring system on-line of grinding robot's movement. In this dissertation, Solid Edge software and its secondary development technology are used to establish the parameters three dimensions model of grinding robot. Then, through five command functions provided openly by Solid Edge software, the grinding robot's monitoring system online are build simply.
引文
1.阎家宾.火箭和航天工程用低密度有机硅隔热弹性材料[J],世界橡胶工业,2006,33(6)：17-20.
2.梁彦,张弛,张明.固体火箭发动机燃烧室绝热层的设计与研究[J],飞航导弹,2004,9：60-64.
3.陈刚,赵珂,肖志红.固体火箭发动机壳体复合材料发展研究[J],航空制造技术,2004,3：18-22.
4.伍建新.固体火箭发动机整形机床机电系统动力学分析与优化[D],内蒙古工业大学(同时被中国优秀博士学位论文全文数据库收录),2007.
5.解红雨.固体火箭发动机分布式集成设计平台及其关键技术研究[D],北京国防科学技术大学,2006.
6.丁宁.外圆纵向智能磨削研究[D],长春吉林大学,2004.
7. Ghani J A, Chaudhoury I A, Hassan H H. Application of taguchi method in the optimization of end milling parameters [J], Journal of Materials Processing Technology.2004,145:84-92.
8. Ren X, Kuhlenkotter B, Miiller H. Simulation and verification of belt grinding with industrial robots [J], International Journal of Machine Tools and Manufacturev,2006,46:708-716.
9.罗送保,张建明.球面曲面光学零件超精密加工装备与技术[J],光学精密工程,2003,11(1)：75-77.
10.贾世奎,李成贵,刘春红,王民.玻璃材料非球面的加工方法[J],航空精密制造技术,2007,43(5)：15-17.
11. Chiaki Tsuzuku. The trend of robot technology in semi-conductor and LCD industry[J], Industrial Robots,2001,28 (5):406-414.
12.郭应竹,石志奎.航空机载零件磨粒流去毛刺工艺[J],航空制造技术,2001,4:57-59.
13. Weule H, Timmermann S. Automation of the surface finishing in the manufacturing of dies and molds [J], Annals of the CIRP,1990,39:299-302.
14. Kopac J, Bahor M, Sokovic M. Optimal machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel work pieces [J], International Journal of Machine Tools and Manufacture,2002,42:707-796.
15. TsaiMJ, Chang J Lung, Huang J F. Development of an automated mold polishing system[J], IEEE Transactions on Automation Science and Engineering,2005,2 (4):393-397.
16.席春江,外国磨床评述,精密制造与自动化[J],2007,4：5-8.
17.詹建明,赵继,祝佩兴.机器人超声研抛自由曲面的精加工系统[J],中国机械工程,2000,115(8)：852-855.
18. Allen Y Y, Mike H, Tom P. A computer controlled optical pin polishing machine [J], Journal of Materials Processing Technology,2004,146:156-162.
19.宋红文,夏季,刘继光.航空发动机叶片三维强化抛光技术[J],航空制造技术,2005,5：61-63.
20.王树国,付宜利.我国特种机器人的发展战略思考[J],自动化学报,2002,12(28)：70-76.
21. Dario P, Carrozza M C, Laschi C, et al. Eu views and strategy for the future of advanced robotics [C], IEEE Workshop on Advanced Robotics and its Social Impacts, Kobe, Japan,2005,8-11.
22. Bailak G V, Rubinger B, Jang M, et al. Advanced robotics mechatronics system: emerging technologies of interplanetary robotics[C], Conference on Electrical and Computer Engineering,2004, Canadian,2004,4:2025-2028.
23.王田苗.全力推进我国机器人技术[J],机器人技术与应用,2007：17-23.
24. Pegman G. Advanced robotics in the UK opportunities and challenges[C], IET Seminar on Directions and Funding of Robotics Research in the UK, Bristol, UK,2008:1-14.
25. Eun J K, Youngil Y. Simulation study of fish swimming modes for aquatic robot system[C],12th International Conference on Advanced Robotics, Seattle, USA,2005:39-44.
26. Chang K H, Chen Y Y. Efficiency on snake robot locomotion with constant and variable bending angles[C], IEEE Workshop on Advanced Robotics and Its Social Impacts, Taipei, Taiwan,2008:1-5.
27.张云伟.煤气管道检测机器人系统及其运动控制技术研究[D],上海交通大学,2007.
28. Du Q J, Zhang X Y, Li Z. Design optimization of a minimally invasive surgical robot [C], IEEE International Conference on Integration Technology, Shenzhen, China,2007:179-184.
29. Lopes L S, Connell J H, Dario P, et al. Sentience in robots:applications and challenges [J], IEEE Intelligent Systems,2001,16(5):66-69.
30.张英.水下仿生拱泥机器人及其关键技术研究[D],武汉理工大学,2005.
31. Hirsch U, Kuntze H B, Brecht R, Kilthau A. Structure and parameter optimization of an active adsorber for decentralized vibration control [J], Robotics and Automation,1992,1(2):670-675.
32.李斌,佟杰新.大型机械系统整体优化策略的研究[J],机械工程学报,1996,32(1)：56-61.
33. Li Y M, Xu Q S. Design and application of a new 3-DOF translational parallel manipulator[C], IEEE Workshop on Advanced Robotics and Its Social Impacts, Hsinchu, Taiwan,2007:1-6.
34.刘惟信.机械最优化设计[M],北京：清华大学出版社,1993,195-224.
35. Sergey E L. Optimal structural synthesis, modeling and control of micro-mechatronic systems[J] Mechatronics,2001,11 (7):837-851.
36.孙焕纯,柴山,王跃方.离散变量结构优化设计[M],大连：大连理工大学出版社,1995,1-13.
37. Coelho R F, Bersini H, Bouillard P H. Parametrical mechanical design with constraints and preferences:application to a purge valve[J], Computer Methods in Applied Mechanics and Engineering,2003,192:4355-4378.
38. Fu K, Mills J K. A convex approach solving simultaneous mechanical structure and control system design problems with multiple[J], Journal of Dynamic Systems, Measuremen and Control,2005 127(1):57-68.
39. He Y P, Mcphee J. Multidisciplinary design optimization of mechatronic vehicles with active suspensions [J], Journal of Sound and Vibration,2005,283:217-241.
40.陈立周,等.工程离散变量优化设计方法--原理与应用[M],北京：机械工业出版社,1989,1-21,108-133.
41. Fogel D B. Evolutionary computation:toward a new philosophy of machine intelligence[M], New York:IEEE Press on Neural Networks Council,1995,1-60.
42.王凌.智能优化算法及其应用[M],北京：清华大学出版社,2001,1-15,20-35.
43. Asuni G, Teti G, Laschi C, et al. Extension to end-effector position and orientation control of a learning-based neurocontroller for a humanoid arm [C], IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China,2006:4151-4156.
44.唐文艳,顾元宪.遗传算法在结构优化中的研究进展[J],力学进展,2002,32(1)：26-40.
45. Kicinger R, Arciszewski T, De Jong. Evolutionary designing of steel structures in tall buildings [J], Journal of Computing in Civil Engineering,2003,19(3):223-238.
46.郭海丁,路志峰.基于BP神经网络和遗传算法的结构优化设计[J],航空动力学报,2003,18(2)：216-220.
47.马光文,王黎.遗传算法在桁架结构优化设计中的应用[J],工程力学,1998,15(2)：38-44.
48. Saridis G N. Intelligent robotic control [J], IEEE transaction on Automatic Control,1983,28(5): 547-557.
49. Rafael V G, Rodrigo M B, Alessandro G. An intelligent framework for simulating robot-assisted surgical operations[J], Expert Systems with Applications,28 (3):425-433.
50. Hani H, Tarek S. Intelligent learning and control of autonomous robotic agents operating in unstructured environments [J], Information Sciences,145 (1):1-12.
51. Kyoung K. A, Huynh T, Chau N. Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network[J], Mechatronics,17:255-262.
52. Hogan N. Impedance Control:An Approach to Manipulation:Part Ⅰ-theory. Part Ⅱ-Implementation. Part Ⅲ-Applications [J], Journal of Dynamic Systems, Measurement and Control,1985:1-24.
53. Chan S P, Yao B, Gao W B. Robust impedance control of robot manipulators [J], Journal of Robotice Automation,1991,6(4):220-227.
54.胡建元,黄心汉,陈锦江.机器人的力控制和顺应控制研究进展[J],机器人,1992,14(2)：52-57.
55. Seraji H, Colbaugh R. Force tracking in impedance control [J], Journal of Robot Research,1997, 16(1):97-117.
56. Baptista L F, Sousa J M, Costa J. Force control of robotic manipulators using a fuzzy predictive approach[J], Journal of Intelligent and Robotics System,2001,30:359-376.
57. Huang L, Ge S S, Lee T H. Neural network based adaptive impedance control of constrained robots [C], Proceedings of IEEE Internatinal Symposium on Intelligent Control, Vancouver, Canada,2002: 615-619.
58.温淑焕.不确定性机器人力/位置智能控制及轨迹跟踪实验的研究[D],燕山大学,2005.
59. Huang L, GeSS, Lee TH. Fuzzy unidirectional force control of constrained robotic manipulators[J], Fuzzy Sets and Systems,2003,134:135-146.
60. Seul J. Robust neural force control scheme under uncertainties in robot dynamics and unknown environment[J], IEEE Transactions on Industrial Electronics,2000,47(4):403-412.
61. Wang H, Low K H, Michael Yu Wang. A bilateral teleoperation controller considering the transition between the free space motion and the constrained motion, Robotica,2008,26:781-790.
62.殷跃红,朱剑英.智能机器力觉及力控制研究综述[J],航空学报,1999,20(1)：1-7.
63. Ohba K, Ortega C, Tanie K, Rin G, et. Real-time micro observation technique for tele-micro-operation [C], IEEE/RSJ International Conference on Intelligent Robots and Systems,2000, 1: 647-652.
64.孙怀安,杨广平.基于虚拟现实语言的机器人三维仿真系统软件[J],西安电子科技大学学报(自然科学版),2001,6：364-369.
65.刘振宇,徐方,陈英林.一种通用的机器人三维图形仿真的实现[J],机器人,2001,9：404-409.
66.任倩,王树国,陈祥立,等.基于虚拟现实的机器人离线编程技术的研究[J],机器人,2003,3：172-177.
67. Mollet N, Brayda L G, Khelifa B, et al. Standardization and Integration in Robotics:Case of Virtual Reality Tools [C], International Conference on Cyberworlds, Hangzhou, China,2008:525-530.
68. Tsai M J, Nai J A. An automatic golf head robotic welding system using 3D machine vision system[C], IEEE Workshop on Advanced Robotics and Its Social Impacts, Taipei, Taiwan,2008: 1-6.
69.吴晓,丁国富,王金诺.基于VR技术的虚拟远程物料搬运系统研究[J],中国远洋,2001：50-51.
70.梁宏宝,钟诗胜,王知行.虚拟机械加工环境的建造[J],高技术通讯,2001：74-76.
71.蔡白兴.机器人学[M],北京：清华大学出版社,2000：1-25,30-55.
72.柳洪义,宋伟刚.机器人技术基础[M],北京：冶金工业出版社,2002：1-15,20-35.
73.方建军,何广平.机器人技术基础[M],北京：化学工业出版社,2004：1-13,88-92.
74.郭治.产业机器人、服务机器人和特种机器人[M],天津：天津科技翻译出版公司,1999：106-146.
75.冈本嗣男,等.生物农业智能机器人[M],北京：科学技术文献出版社,1994：6-28,223-233.
76. He Y P, Mcphee J. Multidisciplinary design optimization of mechatronic vehicles with active suspensions [J], Journal of Sound and Vibration,2005,283:217-241.
77.龚振邦,汪勤悫,陈振华,等.机器人机械设计[M],北京：电子工业出版社,1995：
78.张铁,谢存禧.机器人学[M],广州：华南理工大学出版社,2001：9-38,42-55.
79.杨延力.机器人机构拓扑结构学[M],北京：机械工业出版社,2004：1-15.
80.白象忠.材料力学[M],北京：科学出版社,2007,1-30,45-80.
81.张义民.机械振动[M],北京：清华大学出版社,2007,1-45.
82.马香峰.机器人机构学[M],北京：机械工业出版社,1991,1-50.
83.谢存禧,李琳.空间机构设计与应用创新[M],北京：机械工业出版社,2008,1-25,68-85.
84.陈立周.机械优化设计方法[M],北京：冶金工业出版社,2005,1-22,132-137,147-155,170-183,196-213.
85.胡毓达.实用多目标最优化[M],上海,上海科学技术出版社,1990,1-35.
86. Coello C A, Christiansen A D. Multiobjective optimization of trusses using genetic algorithms [J], Computers & Structures,75(6):647-660.
87. Deb K. An efficient constraint handling method for genetic algorithms [J], Computer Methods in Applied Mechanics and Engineering,2000,186:311-338.
88.林丹,李敏强,·寇纪凇.基于遗传算法求解约束优化问题的一种算法[J],软件学报,2001,12(4)：628-632.
89.方世杰,綦耀光.机械优化设计[M],北京：机械工业出版社,2004：1-12,94-112,145-153.
90. Gantovnik V B, Anderson C, Christine M, et al. A genetic algorithm with memory for mixed discrete-continuous design optimization[J], Computer Science,2003:2003-2009.
91.卢险峰.最优化方法应用基础[M],上海：同济大学出版社,2003：141-148,162-188.
92.陈新度,秦叶,陈新,等.遗传算法在离散变量结构优化设计中的应用[J],华中理工大学学报,1997,25(8)：4446.
93.莫鸿强.遗传算法搜索能力和编码方式研究[D],华南理工大学,2001.
94. Gelenbe E, Liu P, Laine J. Genetic algorithms for route discovery[J], IEEE Transactions on Systems, Man, and Cybernetics, Part B,2006,36(6):1247-1254.
95.陈伦军.机械优化设计遗传算法[M],北京：机械工业出版社,2005,1-28,55-110.
96.雷德明.多维实数编码遗传算法[J],控制与决策,2000,15(2)：239-241.
97. Damousis I G, Bakirtzis A G, Dokopoulos P S. Network-constrained economic dispatch using real-coded genetic algorithm [J], IEEE Transactions on Power Systems,2003,18 (1):198-205.
98.唐飞.十进制编码遗传算法的模式定理研究[J],小型计算机系统,2000,21(4)：364-367.
99. Chen K S, He Z, Han C. A modified real GA for the sparse linear array synthesis with multiple constraints [J], IEEE Transactions on Antennas and Propagation,2006,54 (7):2169-2173.
100.雷英杰.遗传算法工具箱应用,西安：西安电子科技大学出版社,2005,1-23,45-66,80-95.
101. Wang Y T, Jan Y J. Path planning for robot-assist grinding processes[C],, IEEE International Conference on Robot & Automation, Korea, Seoul,2001:331-336.
102. Liu Y Q, Teng J H, Song W L. Trajectory planning and optimal method of spray manipulator for furniture surface painting[C], Second IEEE Conference on Industrial Electronics and Applications, 2007:2030-2035.
103. Kim D L, Jeon J W. Kim S. Software Linear and Exponential Acceleration/Deceleration Methods for Industrial Robots and CNC Machine Tools[J], Mechatronics,1994,4:37-53.
104. Dong-Ⅱ K. Study on interpolation algorithms of CNC machine Tools[C], Thirtieth IEEE Conference Record on IAS, Orlando, USA,1995:1930-1937.
105.周正干,李和平,王美清,等.提高数控加工速度和精度的几种方法[J],制造技术与机床,2001,12：38-40.
106. Jeon J W, Kim Y K. FPGA-based acceleration and deceleration circuit for industrial robots and CNC machine tools[J], Mechatrionics,2002,12(4):635-642.
107.叶献方,吴凌云,陈绪兵.运动控制中的速度分析与设计[J],中国机械工程,2002,13(6)520-523.
108. Erkorkmaz A. High speed CNC system design Part1: Jerk limited trajetory generation and quintic spline interpolation [J], International Journal of Mahine Tools and Manufature,2001,41(9): 132-1345.
109.陈友东,王田苗,魏洪兴,等.数控系统的直线和S形加减速研究[J],中国机械工程,2006,17(15)：1600-1604.
110. Tounsi N, Elbestawi M A. Optimized feed scheduling in three axes machining Part Ⅰ: Fundamentals of the optimized feed scheduling strategy[J], International Journal of Machine Tools & Manufacture, 2003, (43):253-267.
111.石川,赵彤叶,叶佩青,等.数控系统S曲线加减速规划研究[J],中国机械工程,2007,18(10)：1421-1425.
112.孙一兰.多轴控制系统的研究[D],沈阳东北大学,2005.
113. GuoXG, Wang D C, Li C X, et al. A rapid and accurate positioning method with linear deceleration in servo system[J], International Journal of Machine Tools and Manufacture,2002,42: 851-861.
114. Jeon J W. Efficient acceleration and deceleration technique for short distance movement in CNC machine tools [J], Electronics Letters,2000,36(8):766-768.
115.叶佩青,赵慎良.微小直线段的连续插补控制算法研究[J],中国机械工程,2004,15(15)：1354-1356.
116.王宇晗,肖凌剑,曾水生,等.小线段高速加工速度衔接数学模型[J],上海交通大学学报,2004,38(6)：901-904.
117.杨开明,石川,叶佩青,等.数控系统轨迹段光滑转接控制算法[J],清华大学学报(自然科学版),2007,47(8)：1295-1299.
118.何英武,郭远东.插补过程用后加减速时精度控制方法研究与实现[J],制造技术与机床,2006,10：117-120.
119. Yong T, Narayanaswami R. Aparametric interpolator with confined chord errors, acceleration and deceleration for NC machining[J], CAD Computer Aided Design,2003,35(13):1249-1259.
120.张彦博.等弧长椭圆时间分割插补算法[J],机床与液压,2005,7：41-42.
121.游有鹏,王珉.时间分割法椭圆曲线插补算法[J],机械制造,1999,2：14-16.
122. Belmans P, Culioli J. Alternative method to solving the kinematics of a redundant robot[C], Proceedings of the 38th Conference on Remote Systems Technology, Nashville, USA,1990:97-99.
123. Kircanski M V, Petrovic T M. Inverse kinematics solution for a 7 DOF robot with minimal computational complexity and singularity avoidance[C], Proceedings of IEEE International Conference on Robotics and Automation, Sacramento, USA,1991:2664-2669.
124. Sakurama K, Nakano K. Online modification of reference trajectories of multiple robots for collision avoidance[C], Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007:1416-1422.
125.刘永,王克鸿,杨静宇.冗余度弧焊机器人位置优化建模及算法[J],焊接学报,2007,28(11)：25-28.
126. Liu X J. Optimal kinematic design of a three translational DOF parallel manipulator [J], IEEE Transactions on Robotics,2006,24 (2):239-250.
127.邱白晶,钱国宏,周宁,等.割草机器人避障控制[J],江苏大学学报(自然科学版),2005,26(4)：285-288.
128. Jong K P, James E Bobrow. Reliable computation of minimum-time motions for manipulators moving in obstacle fields using a successive search for mimimum-overload trajectories [J], Journal of Robotic Systems,2005,22 (1):1-14.
129. Alan B, Oussama K. Design of non-redundant manipulators for optimal dynamic performance [C], 8th International Conference on Advanced Robotics, Monterey, CA,1997:865-872.
130. Paul I R, Byung R L, Mark A S. Two-arm kinematic posture optimization for fixtureless assembly [J], Journal of Robotic Systems,1995,12 (1):55-65.
131. Li J, Liu Y J, Wang C Q, et al. Optimal kinematic design of a planar parallel manipulator with high speed and high precision [C], Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, China, Luoyang,2006:1888-1892.
132. Busklewicz J. A method of optimization of solving a kinematic problem with the use of structural analysis algorithm[J], Mechanism and Machine Theory,2006,41 (7):823-837.
133. Asokan T, Seet G, Lau M, et al. Optimum positioning of an underwater intervention robot to maximise workspace manipulability[J], Mechatronics,2005,15(6):747-766.
134. Asuni G, Teti G, Laschi C, et al. Extension to end-effector position and orientation control of a learning-based neurocontroller for a humanoid arm [C], IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China,2006:4151-4156.
135. Asokan T, Seet G, Lau M, et al. Optimum positioning of an underwater intervention robot to maximise workspace manipulability[J], Mechatronics,2005,15(6):747-766.
136. Hogan N. Impedance control:An Approach to Manipulation[C], American Control Conference, San Diego, USA,1984:304-313.
137. Lemay M A, Hogan N, Arian van Dorsten J W. Issues in impedance selection and input devices for multijoint powered orthotics[J], Rehabilitation Engineering, IEEE Transactions on [see also IEEE Trans. on Neural Systems and Rehabilitation],1998,6(1):102-105.
138. Huang L, Ge S S, Lee T H. Neural network adaptive impedance control of constrained robots [J], International Journal of Robotics and Automation,2004,19:117-124.
139. Seul J. Force tracking impedance control for robot manipulators with an unknown environment: theory, Simulation and Experiment[J], The International Journal of Robotics Research,2004,20(9): 765-774.
140.王磊.未知环境下的机器人智能力控制算法、仿真及实验系统[D],沈阳东北大学,2006.
141. Marcu C, Lazea G h., Robotin R. An openGL application for industrial robots simulation[C], IEEE International Conference on Automation, Quality and Testing,2006:254—259.
142.任倩,王树国,陈祥立,等.基于虚拟现实的机器人离线编程技术的研究[J],机器人,2003,3：172-177.
143. Ling Q H, Meng M.Q H, Mei T, Lu H R.3D simulation design based on openGL for four-legged robot[J], IEEE International Conference on Robotics and Biomimetics, Beijing, China,2005:713-717.
144.焦恩璋.以AutoCAD为平台的机器人运动仿真研究[J],计算机辅助设计’与图形学学报,2001,10：932-936.
145.徐益,颜文俊,诸静.机器人三维图形仿真的实现[J],计算机仿真,2003,7：72-75.
146. Chen Y M, Zhang T, Wang D, HeYY. A robot simulation,monitoring and control system based on network and Java 3D[C], Proceeding of the 4th World Congress on Intelligent Control and Automation, Shanghai, China,2002:139-143.
147.张小红,陈贤忠,李则鸣.VB6.0中如何使用ActiveX Automation实现应用程序数据的接口[J],计算机与现代化,2002,8：44-45.
148.赵健.基于ActiveX Automation的Solid Edge二次开发技术[J],郑州大学学报(工学版),2004,3：82-84.
149.彭颖红Solid Edge Visual Basic开发入门[M],北京：科学出版社,2001：1-15,35-65,80-125.
150.郝钢,韩秋实,孙志永.基于PMAC的开放式数控系统性能的研究[J],北京机械工业学院学报,2003,6：24-28.
151. Panasonic交流伺服电机驱动器MINASA系列使用说明书[M]：附35.
152.罗忠.基于超声波传感器的机器人探测与控制研究[D],沈阳东北大学,2006.
2.梁彦,张弛,张明.固体火箭发动机燃烧室绝热层的设计与研究[J],飞航导弹,2004,9：60-64.
3.陈刚,赵珂,肖志红.固体火箭发动机壳体复合材料发展研究[J],航空制造技术,2004,3：18-22.
4.伍建新.固体火箭发动机整形机床机电系统动力学分析与优化[D],内蒙古工业大学(同时被中国优秀博士学位论文全文数据库收录),2007.
5.解红雨.固体火箭发动机分布式集成设计平台及其关键技术研究[D],北京国防科学技术大学,2006.
6.丁宁.外圆纵向智能磨削研究[D],长春吉林大学,2004.
7. Ghani J A, Chaudhoury I A, Hassan H H. Application of taguchi method in the optimization of end milling parameters [J], Journal of Materials Processing Technology.2004,145:84-92.
8. Ren X, Kuhlenkotter B, Miiller H. Simulation and verification of belt grinding with industrial robots [J], International Journal of Machine Tools and Manufacturev,2006,46:708-716.
9.罗送保,张建明.球面曲面光学零件超精密加工装备与技术[J],光学精密工程,2003,11(1)：75-77.
10.贾世奎,李成贵,刘春红,王民.玻璃材料非球面的加工方法[J],航空精密制造技术,2007,43(5)：15-17.
11. Chiaki Tsuzuku. The trend of robot technology in semi-conductor and LCD industry[J], Industrial Robots,2001,28 (5):406-414.
12.郭应竹,石志奎.航空机载零件磨粒流去毛刺工艺[J],航空制造技术,2001,4:57-59.
13. Weule H, Timmermann S. Automation of the surface finishing in the manufacturing of dies and molds [J], Annals of the CIRP,1990,39:299-302.
14. Kopac J, Bahor M, Sokovic M. Optimal machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel work pieces [J], International Journal of Machine Tools and Manufacture,2002,42:707-796.
15. TsaiMJ, Chang J Lung, Huang J F. Development of an automated mold polishing system[J], IEEE Transactions on Automation Science and Engineering,2005,2 (4):393-397.
16.席春江,外国磨床评述,精密制造与自动化[J],2007,4：5-8.
17.詹建明,赵继,祝佩兴.机器人超声研抛自由曲面的精加工系统[J],中国机械工程,2000,115(8)：852-855.
18. Allen Y Y, Mike H, Tom P. A computer controlled optical pin polishing machine [J], Journal of Materials Processing Technology,2004,146:156-162.
19.宋红文,夏季,刘继光.航空发动机叶片三维强化抛光技术[J],航空制造技术,2005,5：61-63.
20.王树国,付宜利.我国特种机器人的发展战略思考[J],自动化学报,2002,12(28)：70-76.
21. Dario P, Carrozza M C, Laschi C, et al. Eu views and strategy for the future of advanced robotics [C], IEEE Workshop on Advanced Robotics and its Social Impacts, Kobe, Japan,2005,8-11.
22. Bailak G V, Rubinger B, Jang M, et al. Advanced robotics mechatronics system: emerging technologies of interplanetary robotics[C], Conference on Electrical and Computer Engineering,2004, Canadian,2004,4:2025-2028.
23.王田苗.全力推进我国机器人技术[J],机器人技术与应用,2007：17-23.
24. Pegman G. Advanced robotics in the UK opportunities and challenges[C], IET Seminar on Directions and Funding of Robotics Research in the UK, Bristol, UK,2008:1-14.
25. Eun J K, Youngil Y. Simulation study of fish swimming modes for aquatic robot system[C],12th International Conference on Advanced Robotics, Seattle, USA,2005:39-44.
26. Chang K H, Chen Y Y. Efficiency on snake robot locomotion with constant and variable bending angles[C], IEEE Workshop on Advanced Robotics and Its Social Impacts, Taipei, Taiwan,2008:1-5.
27.张云伟.煤气管道检测机器人系统及其运动控制技术研究[D],上海交通大学,2007.
28. Du Q J, Zhang X Y, Li Z. Design optimization of a minimally invasive surgical robot [C], IEEE International Conference on Integration Technology, Shenzhen, China,2007:179-184.
29. Lopes L S, Connell J H, Dario P, et al. Sentience in robots:applications and challenges [J], IEEE Intelligent Systems,2001,16(5):66-69.
30.张英.水下仿生拱泥机器人及其关键技术研究[D],武汉理工大学,2005.
31. Hirsch U, Kuntze H B, Brecht R, Kilthau A. Structure and parameter optimization of an active adsorber for decentralized vibration control [J], Robotics and Automation,1992,1(2):670-675.
32.李斌,佟杰新.大型机械系统整体优化策略的研究[J],机械工程学报,1996,32(1)：56-61.
33. Li Y M, Xu Q S. Design and application of a new 3-DOF translational parallel manipulator[C], IEEE Workshop on Advanced Robotics and Its Social Impacts, Hsinchu, Taiwan,2007:1-6.
34.刘惟信.机械最优化设计[M],北京：清华大学出版社,1993,195-224.
35. Sergey E L. Optimal structural synthesis, modeling and control of micro-mechatronic systems[J] Mechatronics,2001,11 (7):837-851.
36.孙焕纯,柴山,王跃方.离散变量结构优化设计[M],大连：大连理工大学出版社,1995,1-13.
37. Coelho R F, Bersini H, Bouillard P H. Parametrical mechanical design with constraints and preferences:application to a purge valve[J], Computer Methods in Applied Mechanics and Engineering,2003,192:4355-4378.
38. Fu K, Mills J K. A convex approach solving simultaneous mechanical structure and control system design problems with multiple[J], Journal of Dynamic Systems, Measuremen and Control,2005 127(1):57-68.
39. He Y P, Mcphee J. Multidisciplinary design optimization of mechatronic vehicles with active suspensions [J], Journal of Sound and Vibration,2005,283:217-241.
40.陈立周,等.工程离散变量优化设计方法--原理与应用[M],北京：机械工业出版社,1989,1-21,108-133.
41. Fogel D B. Evolutionary computation:toward a new philosophy of machine intelligence[M], New York:IEEE Press on Neural Networks Council,1995,1-60.
42.王凌.智能优化算法及其应用[M],北京：清华大学出版社,2001,1-15,20-35.
43. Asuni G, Teti G, Laschi C, et al. Extension to end-effector position and orientation control of a learning-based neurocontroller for a humanoid arm [C], IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China,2006:4151-4156.
44.唐文艳,顾元宪.遗传算法在结构优化中的研究进展[J],力学进展,2002,32(1)：26-40.
45. Kicinger R, Arciszewski T, De Jong. Evolutionary designing of steel structures in tall buildings [J], Journal of Computing in Civil Engineering,2003,19(3):223-238.
46.郭海丁,路志峰.基于BP神经网络和遗传算法的结构优化设计[J],航空动力学报,2003,18(2)：216-220.
47.马光文,王黎.遗传算法在桁架结构优化设计中的应用[J],工程力学,1998,15(2)：38-44.
48. Saridis G N. Intelligent robotic control [J], IEEE transaction on Automatic Control,1983,28(5): 547-557.
49. Rafael V G, Rodrigo M B, Alessandro G. An intelligent framework for simulating robot-assisted surgical operations[J], Expert Systems with Applications,28 (3):425-433.
50. Hani H, Tarek S. Intelligent learning and control of autonomous robotic agents operating in unstructured environments [J], Information Sciences,145 (1):1-12.
51. Kyoung K. A, Huynh T, Chau N. Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network[J], Mechatronics,17:255-262.
52. Hogan N. Impedance Control:An Approach to Manipulation:Part Ⅰ-theory. Part Ⅱ-Implementation. Part Ⅲ-Applications [J], Journal of Dynamic Systems, Measurement and Control,1985:1-24.
53. Chan S P, Yao B, Gao W B. Robust impedance control of robot manipulators [J], Journal of Robotice Automation,1991,6(4):220-227.
54.胡建元,黄心汉,陈锦江.机器人的力控制和顺应控制研究进展[J],机器人,1992,14(2)：52-57.
55. Seraji H, Colbaugh R. Force tracking in impedance control [J], Journal of Robot Research,1997, 16(1):97-117.
56. Baptista L F, Sousa J M, Costa J. Force control of robotic manipulators using a fuzzy predictive approach[J], Journal of Intelligent and Robotics System,2001,30:359-376.
57. Huang L, Ge S S, Lee T H. Neural network based adaptive impedance control of constrained robots [C], Proceedings of IEEE Internatinal Symposium on Intelligent Control, Vancouver, Canada,2002: 615-619.
58.温淑焕.不确定性机器人力/位置智能控制及轨迹跟踪实验的研究[D],燕山大学,2005.
59. Huang L, GeSS, Lee TH. Fuzzy unidirectional force control of constrained robotic manipulators[J], Fuzzy Sets and Systems,2003,134:135-146.
60. Seul J. Robust neural force control scheme under uncertainties in robot dynamics and unknown environment[J], IEEE Transactions on Industrial Electronics,2000,47(4):403-412.
61. Wang H, Low K H, Michael Yu Wang. A bilateral teleoperation controller considering the transition between the free space motion and the constrained motion, Robotica,2008,26:781-790.
62.殷跃红,朱剑英.智能机器力觉及力控制研究综述[J],航空学报,1999,20(1)：1-7.
63. Ohba K, Ortega C, Tanie K, Rin G, et. Real-time micro observation technique for tele-micro-operation [C], IEEE/RSJ International Conference on Intelligent Robots and Systems,2000, 1: 647-652.
64.孙怀安,杨广平.基于虚拟现实语言的机器人三维仿真系统软件[J],西安电子科技大学学报(自然科学版),2001,6：364-369.
65.刘振宇,徐方,陈英林.一种通用的机器人三维图形仿真的实现[J],机器人,2001,9：404-409.
66.任倩,王树国,陈祥立,等.基于虚拟现实的机器人离线编程技术的研究[J],机器人,2003,3：172-177.
67. Mollet N, Brayda L G, Khelifa B, et al. Standardization and Integration in Robotics:Case of Virtual Reality Tools [C], International Conference on Cyberworlds, Hangzhou, China,2008:525-530.
68. Tsai M J, Nai J A. An automatic golf head robotic welding system using 3D machine vision system[C], IEEE Workshop on Advanced Robotics and Its Social Impacts, Taipei, Taiwan,2008: 1-6.
69.吴晓,丁国富,王金诺.基于VR技术的虚拟远程物料搬运系统研究[J],中国远洋,2001：50-51.
70.梁宏宝,钟诗胜,王知行.虚拟机械加工环境的建造[J],高技术通讯,2001：74-76.
71.蔡白兴.机器人学[M],北京：清华大学出版社,2000：1-25,30-55.
72.柳洪义,宋伟刚.机器人技术基础[M],北京：冶金工业出版社,2002：1-15,20-35.
73.方建军,何广平.机器人技术基础[M],北京：化学工业出版社,2004：1-13,88-92.
74.郭治.产业机器人、服务机器人和特种机器人[M],天津：天津科技翻译出版公司,1999：106-146.
75.冈本嗣男,等.生物农业智能机器人[M],北京：科学技术文献出版社,1994：6-28,223-233.
76. He Y P, Mcphee J. Multidisciplinary design optimization of mechatronic vehicles with active suspensions [J], Journal of Sound and Vibration,2005,283:217-241.
77.龚振邦,汪勤悫,陈振华,等.机器人机械设计[M],北京：电子工业出版社,1995：
78.张铁,谢存禧.机器人学[M],广州：华南理工大学出版社,2001：9-38,42-55.
79.杨延力.机器人机构拓扑结构学[M],北京：机械工业出版社,2004：1-15.
80.白象忠.材料力学[M],北京：科学出版社,2007,1-30,45-80.
81.张义民.机械振动[M],北京：清华大学出版社,2007,1-45.
82.马香峰.机器人机构学[M],北京：机械工业出版社,1991,1-50.
83.谢存禧,李琳.空间机构设计与应用创新[M],北京：机械工业出版社,2008,1-25,68-85.
84.陈立周.机械优化设计方法[M],北京：冶金工业出版社,2005,1-22,132-137,147-155,170-183,196-213.
85.胡毓达.实用多目标最优化[M],上海,上海科学技术出版社,1990,1-35.
86. Coello C A, Christiansen A D. Multiobjective optimization of trusses using genetic algorithms [J], Computers & Structures,75(6):647-660.
87. Deb K. An efficient constraint handling method for genetic algorithms [J], Computer Methods in Applied Mechanics and Engineering,2000,186:311-338.
88.林丹,李敏强,·寇纪凇.基于遗传算法求解约束优化问题的一种算法[J],软件学报,2001,12(4)：628-632.
89.方世杰,綦耀光.机械优化设计[M],北京：机械工业出版社,2004：1-12,94-112,145-153.
90. Gantovnik V B, Anderson C, Christine M, et al. A genetic algorithm with memory for mixed discrete-continuous design optimization[J], Computer Science,2003:2003-2009.
91.卢险峰.最优化方法应用基础[M],上海：同济大学出版社,2003：141-148,162-188.
92.陈新度,秦叶,陈新,等.遗传算法在离散变量结构优化设计中的应用[J],华中理工大学学报,1997,25(8)：4446.
93.莫鸿强.遗传算法搜索能力和编码方式研究[D],华南理工大学,2001.
94. Gelenbe E, Liu P, Laine J. Genetic algorithms for route discovery[J], IEEE Transactions on Systems, Man, and Cybernetics, Part B,2006,36(6):1247-1254.
95.陈伦军.机械优化设计遗传算法[M],北京：机械工业出版社,2005,1-28,55-110.
96.雷德明.多维实数编码遗传算法[J],控制与决策,2000,15(2)：239-241.
97. Damousis I G, Bakirtzis A G, Dokopoulos P S. Network-constrained economic dispatch using real-coded genetic algorithm [J], IEEE Transactions on Power Systems,2003,18 (1):198-205.
98.唐飞.十进制编码遗传算法的模式定理研究[J],小型计算机系统,2000,21(4)：364-367.
99. Chen K S, He Z, Han C. A modified real GA for the sparse linear array synthesis with multiple constraints [J], IEEE Transactions on Antennas and Propagation,2006,54 (7):2169-2173.
100.雷英杰.遗传算法工具箱应用,西安：西安电子科技大学出版社,2005,1-23,45-66,80-95.
101. Wang Y T, Jan Y J. Path planning for robot-assist grinding processes[C],, IEEE International Conference on Robot & Automation, Korea, Seoul,2001:331-336.
102. Liu Y Q, Teng J H, Song W L. Trajectory planning and optimal method of spray manipulator for furniture surface painting[C], Second IEEE Conference on Industrial Electronics and Applications, 2007:2030-2035.
103. Kim D L, Jeon J W. Kim S. Software Linear and Exponential Acceleration/Deceleration Methods for Industrial Robots and CNC Machine Tools[J], Mechatronics,1994,4:37-53.
104. Dong-Ⅱ K. Study on interpolation algorithms of CNC machine Tools[C], Thirtieth IEEE Conference Record on IAS, Orlando, USA,1995:1930-1937.
105.周正干,李和平,王美清,等.提高数控加工速度和精度的几种方法[J],制造技术与机床,2001,12：38-40.
106. Jeon J W, Kim Y K. FPGA-based acceleration and deceleration circuit for industrial robots and CNC machine tools[J], Mechatrionics,2002,12(4):635-642.
107.叶献方,吴凌云,陈绪兵.运动控制中的速度分析与设计[J],中国机械工程,2002,13(6)520-523.
108. Erkorkmaz A. High speed CNC system design Part1: Jerk limited trajetory generation and quintic spline interpolation [J], International Journal of Mahine Tools and Manufature,2001,41(9): 132-1345.
109.陈友东,王田苗,魏洪兴,等.数控系统的直线和S形加减速研究[J],中国机械工程,2006,17(15)：1600-1604.
110. Tounsi N, Elbestawi M A. Optimized feed scheduling in three axes machining Part Ⅰ: Fundamentals of the optimized feed scheduling strategy[J], International Journal of Machine Tools & Manufacture, 2003, (43):253-267.
111.石川,赵彤叶,叶佩青,等.数控系统S曲线加减速规划研究[J],中国机械工程,2007,18(10)：1421-1425.
112.孙一兰.多轴控制系统的研究[D],沈阳东北大学,2005.
113. GuoXG, Wang D C, Li C X, et al. A rapid and accurate positioning method with linear deceleration in servo system[J], International Journal of Machine Tools and Manufacture,2002,42: 851-861.
114. Jeon J W. Efficient acceleration and deceleration technique for short distance movement in CNC machine tools [J], Electronics Letters,2000,36(8):766-768.
115.叶佩青,赵慎良.微小直线段的连续插补控制算法研究[J],中国机械工程,2004,15(15)：1354-1356.
116.王宇晗,肖凌剑,曾水生,等.小线段高速加工速度衔接数学模型[J],上海交通大学学报,2004,38(6)：901-904.
117.杨开明,石川,叶佩青,等.数控系统轨迹段光滑转接控制算法[J],清华大学学报(自然科学版),2007,47(8)：1295-1299.
118.何英武,郭远东.插补过程用后加减速时精度控制方法研究与实现[J],制造技术与机床,2006,10：117-120.
119. Yong T, Narayanaswami R. Aparametric interpolator with confined chord errors, acceleration and deceleration for NC machining[J], CAD Computer Aided Design,2003,35(13):1249-1259.
120.张彦博.等弧长椭圆时间分割插补算法[J],机床与液压,2005,7：41-42.
121.游有鹏,王珉.时间分割法椭圆曲线插补算法[J],机械制造,1999,2：14-16.
122. Belmans P, Culioli J. Alternative method to solving the kinematics of a redundant robot[C], Proceedings of the 38th Conference on Remote Systems Technology, Nashville, USA,1990:97-99.
123. Kircanski M V, Petrovic T M. Inverse kinematics solution for a 7 DOF robot with minimal computational complexity and singularity avoidance[C], Proceedings of IEEE International Conference on Robotics and Automation, Sacramento, USA,1991:2664-2669.
124. Sakurama K, Nakano K. Online modification of reference trajectories of multiple robots for collision avoidance[C], Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007:1416-1422.
125.刘永,王克鸿,杨静宇.冗余度弧焊机器人位置优化建模及算法[J],焊接学报,2007,28(11)：25-28.
126. Liu X J. Optimal kinematic design of a three translational DOF parallel manipulator [J], IEEE Transactions on Robotics,2006,24 (2):239-250.
127.邱白晶,钱国宏,周宁,等.割草机器人避障控制[J],江苏大学学报(自然科学版),2005,26(4)：285-288.
128. Jong K P, James E Bobrow. Reliable computation of minimum-time motions for manipulators moving in obstacle fields using a successive search for mimimum-overload trajectories [J], Journal of Robotic Systems,2005,22 (1):1-14.
129. Alan B, Oussama K. Design of non-redundant manipulators for optimal dynamic performance [C], 8th International Conference on Advanced Robotics, Monterey, CA,1997:865-872.
130. Paul I R, Byung R L, Mark A S. Two-arm kinematic posture optimization for fixtureless assembly [J], Journal of Robotic Systems,1995,12 (1):55-65.
131. Li J, Liu Y J, Wang C Q, et al. Optimal kinematic design of a planar parallel manipulator with high speed and high precision [C], Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, China, Luoyang,2006:1888-1892.
132. Busklewicz J. A method of optimization of solving a kinematic problem with the use of structural analysis algorithm[J], Mechanism and Machine Theory,2006,41 (7):823-837.
133. Asokan T, Seet G, Lau M, et al. Optimum positioning of an underwater intervention robot to maximise workspace manipulability[J], Mechatronics,2005,15(6):747-766.
134. Asuni G, Teti G, Laschi C, et al. Extension to end-effector position and orientation control of a learning-based neurocontroller for a humanoid arm [C], IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China,2006:4151-4156.
135. Asokan T, Seet G, Lau M, et al. Optimum positioning of an underwater intervention robot to maximise workspace manipulability[J], Mechatronics,2005,15(6):747-766.
136. Hogan N. Impedance control:An Approach to Manipulation[C], American Control Conference, San Diego, USA,1984:304-313.
137. Lemay M A, Hogan N, Arian van Dorsten J W. Issues in impedance selection and input devices for multijoint powered orthotics[J], Rehabilitation Engineering, IEEE Transactions on [see also IEEE Trans. on Neural Systems and Rehabilitation],1998,6(1):102-105.
138. Huang L, Ge S S, Lee T H. Neural network adaptive impedance control of constrained robots [J], International Journal of Robotics and Automation,2004,19:117-124.
139. Seul J. Force tracking impedance control for robot manipulators with an unknown environment: theory, Simulation and Experiment[J], The International Journal of Robotics Research,2004,20(9): 765-774.
140.王磊.未知环境下的机器人智能力控制算法、仿真及实验系统[D],沈阳东北大学,2006.
141. Marcu C, Lazea G h., Robotin R. An openGL application for industrial robots simulation[C], IEEE International Conference on Automation, Quality and Testing,2006:254—259.
142.任倩,王树国,陈祥立,等.基于虚拟现实的机器人离线编程技术的研究[J],机器人,2003,3：172-177.
143. Ling Q H, Meng M.Q H, Mei T, Lu H R.3D simulation design based on openGL for four-legged robot[J], IEEE International Conference on Robotics and Biomimetics, Beijing, China,2005:713-717.
144.焦恩璋.以AutoCAD为平台的机器人运动仿真研究[J],计算机辅助设计’与图形学学报,2001,10：932-936.
145.徐益,颜文俊,诸静.机器人三维图形仿真的实现[J],计算机仿真,2003,7：72-75.
146. Chen Y M, Zhang T, Wang D, HeYY. A robot simulation,monitoring and control system based on network and Java 3D[C], Proceeding of the 4th World Congress on Intelligent Control and Automation, Shanghai, China,2002:139-143.
147.张小红,陈贤忠,李则鸣.VB6.0中如何使用ActiveX Automation实现应用程序数据的接口[J],计算机与现代化,2002,8：44-45.
148.赵健.基于ActiveX Automation的Solid Edge二次开发技术[J],郑州大学学报(工学版),2004,3：82-84.
149.彭颖红Solid Edge Visual Basic开发入门[M],北京：科学出版社,2001：1-15,35-65,80-125.
150.郝钢,韩秋实,孙志永.基于PMAC的开放式数控系统性能的研究[J],北京机械工业学院学报,2003,6：24-28.
151. Panasonic交流伺服电机驱动器MINASA系列使用说明书[M]：附35.
152.罗忠.基于超声波传感器的机器人探测与控制研究[D],沈阳东北大学,2006.