摘要
移动机械臂是一种兼具可操作性和可移动性的机器人,近10余年来在工业、军事、外太空等领域均受到与日俱增的关注。运动规划是移动机械臂执行任务的重要前提条件之一,其具体内容由任务所决定,受任务所限制。本文就移动机械臂运动规划领域内的若干关键理论与应用进行分析与研究,包括：移动平台的点到点运动规划、搜索任务中的路径规划、机械臂逆运动学求解等。
本文的研究工作可以总结为以下几个方面：
1.体系结构是用于定义机器人系统功能模块间的关系,确定任务执行的模式,是理论与技术的承载框架。在研究了典型体系结构的特点之后,针对移动机械臂及其任务的特点,设计了一种用于移动机械臂的混合式体系结构。以此基础,对关键技术进一步研究提供指导,以便有的放矢,并使该研究具有更强的可拓展性。
2.移动平台路径规划是移动机械臂运动规划的基础之一。针对人工势场法存在的问题及势场参数选择对路径规划的影响,提出了一种用量子遗传算法优化势场参数提高路径品质、降低陷入局部极小概率的方法。同时,采取一种用均匀分布的电荷来描述不规则障碍物的方法克服障碍物描述困难的问题。以此为基础,进而构建了一种基于栅格-几何分层地图的路径规划方法,该方法通过人工势场法和改进A*法互补的方式来防止人工势场陷入局部极小,同时降低A’算法的计算量。
3.在移动机械臂的搜索任务中,复杂地图大多存在回路。经研究发现在回路中不同的搜索方向可能导致不同的期望时间,在以往研究中该问题一直没得到关注。通过对该问题的研究,提出了一种回路方向决策方法,并将该方法与一种启发式路径规划方法进行结合以降低其期望时间,然后将该方法用于概率地图中的目标搜索。理论分析、仿真与实验均表明该回路方向决策法能提高原启发式搜索方法的性能,减少搜索所需的期望时间。
4.针对移动机械臂搜索任务中的序列规划问题,提出了一种问题转化框架(MTF),以此建立中国邮递员问题(CPP)与多目标搜索问题的联系。借助于该框架,特征地图可以转换为一种标准拓扑地图,进而可以采用适于CPP的搜索算法来处理移动机器人多目标搜索问题。基于MTF的算法是一种最优算法,且具有与启发式算法近似的多项式复杂度。理论分析与实验表明基于该框架的算法能得到比其他几种算法更短的路径。
5.针对冗余机械臂的全局路径规划问题,提出了一种通用的装载平面冗余机械臂的移动平台的全局规划方案。首先构造了该类型移动机械臂的运动学模型,然后采用第3章所介绍的改进人工势场进行移动平台的路径规划。一种改进的粒子群算法(PSO)则用于求解逆运动学。将该路径规划方法用于一种六连杆移动机械臂模型,并用一种自行构建的仿真平台进行测试。测试表明该方法可用于移动平台的路径规划和平面冗余机械臂的逆运动学求解。
6.提出了一种基于生物信息的免疫克隆算法(ICABBI),并将其用于复杂机械臂的逆运动学求解。ICABBI将环境信息、免疫细胞历史信息和遗传信息引入了克隆选择机制。通过该机制,ICABBI实现了人工免疫系统内部的全局信息交互。用一些经典全局优化问题对该算法测试,测试表明该算法有较好的普适性与高维处理能力。将其用于一种六自由度冗余机械臂的逆运动学,测试表明该算法适于处理这种复杂的非线性全局优化问题,有较高的求解精度。
As a kind of robot with manipulability and mobility, mobile manipulator system has drawn tremendous attention in many domains in the past decade, such as industry, military and deep space. Motion planning, decided and restricted by the task, is an important precondition for the applications of the mobile manipulator. This thesis focuses on several problems in the motion planning of the mobile manipulator, such as point to point motion planning of mobile platform, path planning in the target search task and the inverse kinematics.
Contributions of this thesis can be summarized as follows:
1. Architecture is used to define the relationships among function modules, decide the execution model of tasks, and works as a frame for carrying certain theories and technologies of mobile manipulator. In this study, several classical architectures are reviewed, and then, a hybird architecture is designed according to the characteristics of task executed by the mobile manipulator. This hybird architecture is employed to provide guidance for further detailed studies.
2. Path planning for mobile platform is the foundation of motion planning for mobile manipulator. Considering the problems of artificial potential field (APF) and the influence of its parameters, a new version of APF is presented to improve the quality of the generated path and reduce the probability of appearing local minimum. This improvement is realized by optimizing several parameters using quantum genetic algorithm. To solve the matching problem between APF functions and obstacles, a novel method is proposed for describing obstacles, in which it is assumed that point charges distribute uniformly along the boundaries of obstacles. According to the above theories, a hierarchical path planning strategy based on grid-geometric map is proposed. In this planning strategy, the local minimum problem is avoided and computational consumption of A* is decreased by the cooperation between APF and improved A*.
3. In the target search task with a mobile manipulator, there exist loops in most complicated environments. We found that different directions in loops lead to different expected-time, and no studies so far have been made to solve this problem. Therefore, a direction choosing method is presented, and applied to a heuristic algorithm to reduce its expected-time for target search. This improved heuristic algorithm is employed to plan paths for target search in probabilistic environments. Simulation and experiments demonstrated that this approach can reduce the expected time for target search.
4. Considering the sequence planning problem in target search task of mobile manipulator, a novel map transformation framework (MTF) is proposed. With MTF, a feature map or a topological map can be converted into a standard topological map on which many graph search algorithms suitable for Chinese Postman Problem (CPP) can be employed to carry out path planning for multiple target search. Similar to some heuristic algorithms, the MTF-based algorithm is globally optimal and has polynomial order time complexity. Theoretical analysis and experiments all indicate that the route generated by the MTF-based algorithm is better than those planned by several other target search algorithms.
5. To deal with the global motion planning of redundant mobile manipulator, a general method is proposed for the mobile platform with a planar manipulator. In this method, the model of this kind of mobile manipulator is constructed, the approach discussed in chapter 3 is applied to plan path for mobile platform, and an improved particle swarm optimization (PSO) algorithm is used to solve the inverse kinematics of manipulator. This general method is applied to do motion planning for a robot with a six-link manipulator, and then tested with a simulation platform built by us. Simulation shows that the proposed method can be used to plan a path for the robot and calculate inverse kinematics for this manipulator.
6. A novel immune clonal algorithm, called the immune clonal algorithm based on biological information (ICABBI), is proposed and used to solve the inverse kinematics of complex manipulators. In ICABBI, the computational implementation of the clonal selection principle takes into account environmental information, cell history information and hereditary characteristics. This mechanism realizes information communication in whole artificial immune systems. We tested ICABBI with several classical global optimization problems to analyze its universality. Then, the ICABBI is applied to solve the inverse kinematics of a 6-DOF manipulator. ICABBI has some features that are unique among biology-based methods. It can deal with the some complex nonlinear global optimization problems, such as the inverse kinematics of complex manipulators, and has satisfactory performance on some high-dimensional problems.
引文
[1]Executive summary of world robotics 2010 industrial robots and service robots [R].2011, Available from:http://www.ifr.org/.
[2]神原伸介,张炜.日本工业机器人最新发展动向[J].机器人技术与应用.2008,4：23-25.
[3]唐新华.焊接机器人的现状及发展趋势[J].电焊机.2006,36(4)：43-46.
[4]Siegwart R., Nourbakhsh I. R. Introduction to autonomous mobile robots [M]. Massachusetts:MIT Press,2004.
[5]Anderson G. T., Tunstel E. W. and Wilson E. W. Robot system to search for signs of life on mars [J]. IEEE Transactions on Aerospace and Electronic Systems.2007,22 (12): 23-30.
[6]Nourbakhsh I. R., Sycara K., Koes M., Yong M., Lewis M. and Burion S. Human-robot teaming for search and rescue [J]. IEEE Pervasive Computing.2005,4 (1):72-79.
[7]Sheng L. Robust and intelligent control of mobile manipulators [D]. University of Toronto,2001.
[8]Holmberg R. Design and development of powered-caster holonomic mobile robots [D]. University of Stanford,2000.
[9]Christopher M. Gifford, review of selected mobile robot and robotic manipulator technologies [R]. Technical report.2006,9.
[10]Yamamoto Y., Control and coordination of locomotion and manipulation of a wheeled mobile manipulator [D]. University of Pennsylvania,1994.
[11]Murphy R. R. Introduction to AI robotics [M]. Massachusetts:MIT Press,2002.
[12]宋佐时,易建强,赵冬斌.移动机械手控制研究进展[J].机器人.2003,25(5)：465-469.
[13]Buehler M, Iagnemma K, Singh S. The 2005 DARPA grand challenge:the great robot race [M]. Berlin:Springer-Verlag,2007.
[14]Buehler M, Iagnemma K, Singh S. The DARPA urban challenge:autonomous vehicles in city traffic [M]. Berlin:Springer-Verlag,2009.
[15]Sarmiento A., Espinoza J., Murrieta-Cid R. and Hutchinson S. A motion planning strategy for rapidly finding an object with a mobile manipulator in 3-D environments [A]. Proceedings of the 7th Mexican International Conference on Artificial Intelligence[C].2008,562-572.
[16]Pires E. S., Oliveira P. B. and Machado J. T. Abstract manipulator trajectory planning using a MOEA[J]. Applied Soft Computing.2007,7 (3):659-667.
[17]Xidias E. P., Zacharia and Aspragathos N. Time-Optimal task scheduling for articulated manipulators in environments cluttered with obstacles [J]. Robotica.2009,28(3): 427-440.
[18]Marcos M. G., Machado J. A. T. and Azevedo Perdicoulis T.P. An evolutionary approach for the motion planning of redundant and hyper-redundant manipulators [J]. Nonlinear Dynamics.2010,60 (1-2):115-129.
[19]Xu D., Calderon C. A. A., Gan J. Q. and Hu H. An analysis of the inverse kinematics for a 5-DOF manipulator [J]. International Journal of Automation and Computing.2005,2 (2):114-124.
[20]Craig J. J. Introduction to robotics:mechanics and control [M]. London:Pearson Education,2005.
[21]Borges G. A. and Aldon M. J. Robustified estimation algorithms for mobile robot localization based on geometrical environment maps [J]. Robotics and Autonomous Systems.2003,45(3-4):131-159.
[22]石朝侠,洪炳镕,周彤,王燕清.大规模环境下的拓扑地图创建与导航[J].机器人.2007,29(5)：433-438.
[23]Elfes A. Occupancy grids:a probabilistic framework for robot perception and navigation [D]. Carnegi Mellon University,1989.
[24]蔡自兴,贺汉根,陈虹.未知环境中移动机器人导航控制研究的若干问题[J].控制与决策.2002,17(4)：385-390.
[25]Li S. Y., Zou T. and Yang Y. P. Finding the fuzzy satisfying solutions to constrained optimal control systems and application to robot path planning [J]. International Journal of General Systems.2004,33 (2-3):321-337.
[26]Kim T. H. and Sugie T. Cooperative control for target-capturing task based on a cyclic pursuit strategy [J]. Automatica.2007,43(8):1426-1431.
[27]Perrier, N., Langevin, A. and Amaya, C. Vehicle routing for urban snow plowing operations [J]. Transportation Science.2009,42 (1):44-56.
[28]Sarmiento, A., Murrieta-Cid, R. and Hutchinson, S. An efficient motion strategy to compute expected-time locally optimal continuous search paths in known environments [J]. Journal of Advanced Robotics.2009,23 (12-13):1533-1560.
[29]Thrun S, Montemerlo M, Dahlkamp H, et al. Stanley:the robot that won the DARPA grand challenge [J]. Journal of Field Robotics.2006,23(9):661-692.
[30]Ghallab M., Nau D.and Traverso P. Automated planning:theory and practice [M]. Amsterdam:Elsevier,2004.
[31]LaValle M. S. Planning algorithms [M]. Cambridge:Cambridge University Press,2006.
[32]Crous C. B. Autonomous robot path planning [D]. University of Stellenbosch,2009.
[33]杨翊鹏,李少远.模糊优化算法及其在视觉机器人路径规划中的应用[J].控制与决策.2002,17：723-726.
[34]张纯刚,席裕庚.基于局部探测信息的机器人滚动路径规划[J].自动化学报.2003,29(1)：38-44.
[35]朱大奇,颜明重.移动机器人路径规划技术综述[J].控制与决策.2010,25(7)：961-967.
[36]马昕,宋锐,郭睿,李贻斌.基于免疫自适应遗传算法的机器人栅格地图融合[J].控制理论与应用.2009,26(9)：1004-1008.
[37]李天成,孙树栋,高扬.基于扇形栅格地图的移动机器人全局路径规划[J].机器人.2010,32(4)：547-552.
[38]李劲松,颜国正,吕恬生,宋立博.一种移动机器人全局路径规划新方法[J].机械设计与研究.2009,25(3)：30-32.
[39]Robotin R., Lazea G. and Marcu C. Graph search techniques for mobile robot path planning [J]. Engineering the Future.2010,159-178.
[40]赵娟平,高宪文,刘金刚,符秀辉.移动机器人路径规划的参数模糊自适应窗口蚁群优化算法[J].控制与决策.2011,26(7)：1096-1100.
[41]Juang C. F. and Chang Y. C. Evolutionary group based particle swarm optimized fuzzy controller with application to mobile robot navigation in unknown environments [J]. IEEE Transactions on Fuzzy Systems.2011,19(2):379-392.
[42]Sarmiento, A., Murrieta-Cid, R. and Hutchinson, S. An efficient strategy for rapidly finding an object in a polygonal world [A]. Proceedings of the IEEE International Conference on Intelligent Robots and Systems [C].2003,1153-1158.
[43]张毅,罗元,郑太雄等.移动机器人技术及其应用[M].北京：电子工业出版社,2007.
[44]Coleman D. M. and Wunderlich J. T.O3:An optimal and opportunistic path planner (with obstacle avoidance) using voronoi polygons [A]. Proceedings of the 10th IEEE International Workshop on Advanced Motion Control [C].2008,371-376.
[45]Kathib O. Real-time obstacle avoidance for manipulators and mobile robots [J]. International Journal of Robotics Research.1986,5 (1):90-98.
[46]Warren C. W. Global path planning using artificial potential fields [A]. Proceedings of IEEE International Conference on Robotics and Automation [C].1989,316-321.
[47]Barraquand J., Latombe J. C. A monte-carlo algorithm for path planning with many degrees of freedom [A]. Proceedings of IEEE International Conference on Robotics and Automation[C].1990,1712-1717.
[48]Sato K. Deadlock-free motion planning using the Laplace potential field [J]. Advanced Robotics.1992,449-461.
[49]Ge S. S. and Cui Y. J. New potential functions for mobile robot path planning [J]. IEEE Transactions on robotics and automation.2000,16(5):615-620.
[50]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.
[51]Bourgault F., Goktogan A., Furukawa T. and Durrant- Whyte H. F. Coordinated search for a lost target in a Bayesian world [J]. Advanced Robotics. Advanced Robotics.2004, 18(10),979-1000.
[52]Rogge J. A. and Aeyels D. Multi-robot coverage to locate fixed and moving targets [A]. Proceedings of the IEEE International Conference on Control Applications [C].2009, 902-907.
[53]Ksenia S. and John K. T. Visual search for an object in a 3D environment using a mobile robot [J]. Image Vision Comput.2010,114(5):535-547.
[54]Ishida H., Ushiku T., Toyama S., Taniguchi H.and Moriizumi T. Mobile robot path planning using vision and olfaction to search for a gas source [A]. The 4th IEEE Conference on Sensors [C].2005,1112-1115.
[55]Sarmiento A., Murrieta-Cidz R. and Hutchinsony S. A sample-based convex cover for rapidly finding an object in a 3-D environment [A]. Proceeding of the IEEE Int.conferenceon robotics and automation[C].2005:3486-3491.
[56]Shermer, T. C. Recent results in art galleries [A]. Proceeding of the IEEE [C].1992: 1384-1399.
[57]Arkin R. C. Motor schema-based mobile robot navigation [J]. The International Journal of Robotics Research.1989,8(4):92-112.
[58]Roberts R., Pippin C. and Balch T. Learning outdoor mobile robot behaviors by example [J]. Journal of Field Robotics.2009,26(2):176-195.
[59]王义萍,陈庆伟,胡维礼.机器人行为选择机制综述[J].机器人.2009,31(5)：472-480.
[60]刘松国.六自由度串联机器人运动优化与轨迹跟踪控制研究[D].浙江大学,2009.
[61]Pieper D. The kinematics of manipulators under computer control [D]. Stanford University,1968.
[62]Pieper D. and Roth B. The kinematics of manipulators under computer control [A]. Proceedings of the second international congress on theory of machines and mechanisms [C].1969, pp.159-169.
[63]Manocha D. and John F. C. Efficient inverse kinematics for general 6R manipulators [J]. IEEE Transactions on Robotics and Automation.1994.10(5):648-657.
[64]Duffy J. A displacement analysis of the general spatial 7-link,7-R mechanisms [J]. Mechanisms and Machine Theory.1980,15(3):153-159.
[65]Lee H. Y. Displacement analysis of the general spatial 7-link,7-R mechanisms [J]. Mechanisms and Machine Theory.1988,23(3):219-226.
[66]Manseur R. A robot manipulator with 16 real inverse kinematic solution sets [J]. The International of Robotics Research.1989,8(5):75-79.
[67]Manocha D., Wampler C. and Morgan A.P. Solving the 6R inverse position problem using a generic-case solution methodology [J]. Mechanisms and Machine Theory.1991, 26(1):91-106.
[68]牛玉刚,杨成梧,陈雪如.基于神经网络的不确定机器人自适应滑模控制[J].控制与决策.2001,16(1)：79-82.
[69]徐德,邹伟.室内移动式服务机器人的感知、定位与控制[M].北京：科学出版社,2008.
[70]Carriker W. F., Khosla P. K. and Krogh B. H. Path planning for mobile manipulators for multiple task execution [J]. IEEE Transactions on Robotics and Automation.1991, 7(3):403-408.
[71]Wiens G. J., Jang W. M. Passive joint control of dynamic coupling in mobile robots [J]. International Journal of Robotics Research.1994,13(3):209-220.
[72]Shi X. P., Liu S. R. and Liu F. New robust control strategy of module manipulators via sliding mode control with an extended state observer [J]. Journal of Systems and Control Engineering.2010,224(15):245-255.
[73]Li Z. J., Ge S. S. and Ming A. G. Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators [J]. IEEE Transaction on Systems, Man, and Cybernetics-Part B:Cybernetics.2007,37 (3):607-616.
[74]Seraji H. Motion control of mobile manipulators [A]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems [C].1993,2056-2063.
[75]柳长安,周宏,李国栋.差动驱动式移动机械手的运动规划[J].宇航学报.2004,25(2)：183-186.
[76]Yamamoto Y. and Yun X. P. Coordinating locomotion and manipulation of a mobile manipulator [A]. Proceedings of the 31st Conference on Decision and control [C].1992, 2643-2648.
[77]张硕生,余达太.轮式移动机械手的多点运动规划方法[J].北京科技大学学报.2001,23(2)：177-180.
[78]Bayle B., Fourquet J. Y. and Renaud M. Manipulability of wheeled-mobile manipulators: application to motion generation [J]. The International Journal of Robotics Research. 2003,22(7-8):565-581.
[79]Bayle B., Fourquet J. Y. and Renaud M. Nonholonomic mobile manipulators:kinematics, velocities and redundancies [J]. Journal of Intelligent and Robotic Systems.2003,36(1): 45-63.
[80]De Luca A., Mattone R. and Oriolo G. Control of redundant robots under end-effector commands:A case study in underactuated systems [J]. Applied Mathematics and Computer Science.1997,7(2):225-251.
[81]De Luca A. and Oriolo G. Nonholonomic behavior in redundant robots under kinematic control [J]. IEEE Transactions on Robotics and Automation.1997,13(5):776-782.
[82]De Luca A., Mattone R. and Oriolo G. Steering a class of redundant mechanisms through end-effector generalized forces [J]. IEEE Transactions on Robotics and Automation. 1998,14(2):329-335.
[83]Papadopoulos E., Papadimitriou I., Poulakakis I. Polynomial-based obstacle avoidance techniques for nonholonomic mobile manipulator systems [J]. Robotics and Autonomous Systems.2005,51(4):229-247.
[84]李新春,赵冬斌,易建强.一种全方位移动机械手的可操作度分析[J].中国机械工程.2006,17(14)：1442-1447.
[85]郭丙华,胡跃明.三连杆移动机械臂模型与运动规划[J].控制理论与应用.2005,22(6)：965-968.
[86]张惠娣.未知环境中移动机器人自主导航控制研究[D].华东理工大学,2008.
[87]Borenstein J., Everett H. R. and Feng L. Where am I?:sensors and methods for mobile robot positioning [R]. Technical Report. University of Michigan,1996.
[88]高学海,徐科军,张瀚.基于单目视觉和激光测距仪的位姿测量算法[J].仪器仪表学报.2007,28(8)：1479-1485.
[89]Nilsson N. J. Shakey the robot [R]. Technical Note 323, SRI International, Menlo Park, CA 1984.
[90]Albus J. S., Mecain H. G. and Lumia R. NASA/NBS standard reference model for telerobot control system architecture (NASREM). NBS Technical Note 1235,1987.
[91]Arkin R. C. Behavior-Based Robotics [M]. MIT Press, Cambridge, MA,1998.
[92]Brooks R. A. A robust layered control system for a mobile robot [J]. IEEE Journal of Robotics and Automation.1986,2(1):14-23.
[93]Connell J. Minimalist mobile robotics:a colony architecture for an artificial creature. Amsterdam:Elsevier,1990.
[94]Mataric M J. Reinforcement learning in the multi-robot domain [J]. Autonomous Robots. 1997,4(1):73-83.
[95]Firby R. J., Kahn R. E., Prokopwicz P. N. and Swain M. J. An architecture for vision and action [A]. Proceedings of 1995 International Joint Conference on Artificial Intelligence [C].1995,72-79.
[96]Konolige K. and Myers K. The Saphira architecture for autonomous mobile robots. Artificial Intelligence and Mobile Robots. MIT Press, Cambridge, MA,1998.
[97]Simmons, R. Goodwin R., Haigh K. Z., Koenig S. and O'Sullivan J. A layered architecture for office delivery robots [A]. Proceedings of the First International Conference on Autonomous Agents [C].1997,245-252.
[98]李磊,叶涛,谭民等.移动机器人技术研究现状与未来[J].机器人.2002,24(5)：475-480.
[99]Dedeoglu G., Sukhatme G. S. Landmark based matching algorithm for cooperative mapping by autonomous robots [A]. Proceedings of the 5th International Symposium on Distributed Autonomous Robotic Systems [C].2000,251-260.
[100]Stachniss C. and Burgard W. Mobile robot mapping and localization in non-static environments [A]. Proceedings of the International Conference on Artificial Intelligence [C].2005,1324-1329.
[101]Thrun S. Learning Metric-topological maps for indoor mobile robot navigation [J]. Artificial Intelligence.1998,99(1):21-71.
[102]Blanco J. L. Toward a unified Bayesian approach to hybrid metric-topological SLAM [J]. IEEE Transactions on Robotics.2008,24(2):259-270.
[103]Goedeme T., Nuttin M. and Tuytelaars P. Omnidirectional vision based topological navigation [J]. International Journal of Computer Vision.2007,74(3):219-236.
[104]Ila V., Porta J. M. and Andrade-Cetto J. Information-based compact pose SLAM [J]. IEEE Transactions on Robotics.2010,26(1):78-93.
[105]Cole D. M. and Newman P. M. Using laser range data for 3D SLAM in outdoor environments [A]. Proceedings of the IEEE international conference on robotics and automation [C].2006:1556-1563.
[106]庄严,徐晓东,王伟.移动机器人几何-拓扑混合地图的构建及自定位研究[J].控制与决策.2005,20(7)：815-822.
[107]王醒策,张汝波,顾国昌.基于势场栅格法的机器人全局路径规划[J]_哈尔滨工程大学学报.2003,24(2)：170-174.
[108]Agirrebeitia J., Aviles, R., De Bustos I. F. and Ajuria G. A new APF strategy for path planning in environments with obstacles [J]. Mechanism and Machine Theory.2005, 40 (6),645-658.
[109]Jiao L. C., L. Y. Y., Gong M. G. and Zhang X. G. Quantum inspired immune clonal algorithm for global optimization [J]. IEEE Transactions on System, Man, and Cybernetics, Part B.2008,38(5):1234-1253.
[110]Tovar B., LaValle S. and Murrieta-Cid R. Optimal navigation and object finding without geometric maps or localization [A]. Proceedings of the IEEE International Conference on Robotics and Automation [C].2003,464-470.
[111]Isler V., Kannan S. and Khanna S. Randomized pursuit-evasion in a polygonal environment [J]. IEEE Transactions on Robotics.2005,21(5):875-884.
[112]Comaniciu D., Ramesh V. and Meer P. Real-time tracking of non-rigid objects using mean shift [A]. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition [C]. 2000,142-149.
[113]Zhou L. J., Xie W. X and Li L. Q. Mean shift track initation algorithm based on Hough transform [A]. IEEE 10th International Conference on Signal Processing [C].2010, 1263-1266.
[114]Bradski G. R. Computer vision face tracking for use in a perceptual user interface [J]. Intel Technology Journal.1998,2(2):1-15.
[115]Nouar O. D., Ali G. and Raphael C. Improved object tracking with Camshift algorithm [A]. Proceedings of the IEEE International Conference on Acoustics. Speech and Signal Processing [C].2006,2:657-660.
[116]孙凯,刘士荣.多目标跟踪的改进Camshift/卡尔曼滤波组合算法[J].信息与控制.2009,38(1)：9-14.
[117]Meuth R. J. and Wunsch Ⅱ D. C. Divide and conquer evolutionary TSP solution for vehicle path planning [A]. Proceedings of the IEEE International Conference on Evolutionary Computation [C].2008,676-681.
[118]Zheng X. and Koenig S. Robot coverage of terrain with non-uniform traversability [A]. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems [C].2007,3757-3764.
[119]Munkres M. Algorithms for the assignment and transportation problems [J]. Journal of the Society for Industrial and Applied Mathematics.1957,5 (1):32-38.
[120]Edmonds J. and Johnson E. L. Matching, Euler Tours, and the Chinese Postman [J]. Mathematical Programming.1973,5:88-124.
[121]Sipahioglu A., Kirlik G., Parlaktuna O. and Yazici A. Energy constrained multi-robot sensor-based coverage path planning using capacitated arc routing approach [J]. Robotics and Autonomous Systems.2010,58 (5):529-538.
[122]Fred B. and Marty L. A friendly introduction to graph theory [M]. London:Pearson Ed., 2003.
[123]Lau H., Huang S. and Dissanayake G. Optimal search for multiple targets in a built environment [A]. Proceedings of the IEEE International Conference on Intelligent Robots and Systems [C].2005,3740-3745.
[124]Fukazawa Y., Trevai C., Ota J., Yuasa H., Arai T. and Asama H. Controlling a mobile robot that searches for and rearranges objects with unknown locations and shapes [A]. Proceedings of the IEEE International Conference on Intelligent Robots and Systems [C].2003,1711-1726.
[125]Frese U., Larsson P. and Duckett T. A multilevel relaxation algorithm for simultaneous localization and mapping [J]. IEEE Transactions on Robotics.2005,21 (2):1-12.
[126]Stachniss C. Robotic mapping and exploration [M]. Berlin:Springer-Verlag,2009.
[127]Dennis M. L. Crowding-An essential bottleneck for object recognition:A mini-review [J]. Vision Research.2008,48 (5):635-644.
[128]Farshidia F., Sirouspour S. and Kirubarajana T. Robust sequential view planning for object recognition using multiple cameras [J]. Image and Vision Computing.2009,27 (8):1072-1082.
[129]Yamazaki K., Tomono M., Tsubouchi T. and Yuta S. Motion planning for a mobile manipulator based on joint motions for error recovery [A]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems [C].2006,7-12.
[130]罗小平,韦巍.一种基于生物免疫遗传学的冗余机械手轨迹规划新方法[J].模式识别与人工智能.2002,15(3)：299-303.
[131]周友行,何清华,邓伯禄.一种改进的爬山法优化求解冗余机械手运动学逆解[J].机器人.2003,25(1)：25-38.
[132]Shelokar P. S., Siarry P., Jayaraman V. K., et al. Particle swarm and ant colony algorithms hybridized for improved continuous optimization [J]. Applied Mathematics and Computation.2007,188(1):129-142.
[133]Hart, E. and Timmis, J. Application areas of AIS:the past, the present and the future [J]. Applied Soft Computing.2008,8(1):191-201.
[134]De Castro L. N. and Von Zuben F. J. Artificial Immune Systems:Part Ⅰ-basic theory and applications [R]. Technical Report TR-DCA 01/99, Campinas, Brazil,1999.
[135]De Castro L. N. and Von Zuben F. J. Artificial Immune Systems:Part Ⅱ-a survey of applications [R]. Technical Report DCA-RT 02/00, Campinas, Brazil,2000.
[136]Whitbrook A. M, Aickelin U. and Garibaldi J. M. Idiotypic immune networks in mobile-Robot control [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B. 2007,37(6):1581-1598.
[137]Coello Coello C. A. and Cortes N.C. Solving multiobjective optimization problems using an artificial immune system [J]. Genetic Programming and Evolvable Machines. 2005,6(2):163-190.
[138]Dasgupta D. Cao, Y. and Yang C. An immunogenetic approach to spectra recognition [A]. Proceedings of the Genetic and Evolutionary Computation Conference [C].1999, 149-155.
[139]Guimaraes F. G., Palhares R. M., Campelo F. and Igarashi H. Design of mixed H2/H∞ control systems using algorithms inspired by the immune system [J]. Information Sciences.2007,177(20):4368-4386.
[140]Kim J. and Bentley P. J. Towards an artificial immune system for network intrusion detection:An investigation of clonal selection [A]. Proceedings of the Congress on Evolutionary Computation [C].2001,1244-1252.
[141]Hunt J. E. and Cooke D. E. Learning using an artificial immune system [J]. Journal of Network and Computer Applications.1996,19(2):189-222.
[142]Vito S. D. Martinelli E. Fuccio R. D. Tortorella F. Francia G. D. Amico A. D. and Natale C. D. Artificial immune systems for artificial olfaction data analysis [J]. Computational Intelligence.2010,18-23.
[143]Kahraman C., Engin O. and Yilmaz M. K. A new artificial immune system algorithm for multiobjective fuzzy flow shop problems [J]. International Journal of Computational Intelligence Systems.2009,2(3):236-247.
[144]Xu H. Q., Ding Y. S. and Hu Z. H. Adaptive immune genetic algorithm for logic circuit design [A]. Proceeding of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation [C].2009,639-644.
[145]Burnet F. M. The clonal selection theory of immunity [M]. Cambridge:Cambridge University Press,1959.
[146]De Castro L. N. and Von Zuben F. J. Learning and optimization using the clonal selection principle [J]. IEEE Transaction on Evolutionary Computation.2002,6(3): 239-351.
[147]Gong M. G., Jiao L. C., Du H. F. and Bo L. F. Multiobjective immune algorithm with nondominated neighbor-based selection [J]. Evolutionary Computation.2008,16(2): 225-255.
[148]Cutello V. Nicosia G. Pavone M. and Timmis J. An immune algorithm for protein structure prediction on lattice models [J]. IEEE Transaction on Evolutionary Computation.2007,11(1):101-117.
[149]Ji Z. and Dasgupta D. Revisiting negative selection algorithms [J]. Evolutionary Computation.2007,15(2):223-251.
[150]Fukuda T., Mori K. and Tsukiama, M. Parallel search for multi-modal function optimization with diversity and learning of immune algorithm, Artificial Immune Systems and their Applications [M]. Berlin:Springer Verlagl,1999.
[151]Cutello V. Nicosia G. and Pavone M. An immunological approach to combinatorial optimization problem. Advances in Artificial Intelligence [M]. Berlin:Springer Verlagl,2002.
[152]Cutello V. Nicosia G. and Pavone M. Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator [A]. Proceedings of the 2006 ACM symposium on Applied computing[C]. 2006,950-954.
[153]Khilwani N., Prakash A., Shankar, R. and Tiwari M. K. Fast clonal algorithm [J]. Engineering Applications of Artificial Intelligence.2008,21(1):106-128.
[154]Jerne M. K. Towards a network theory of the immune system [J]. Ann. Immunol.1974, 125C:373-389.
[155]Sprent J. T and B memory cells [J]. Cell.1994,76(2):315-322.
[156]Allen D. et al. Timing, genetic requirements and functional consequences of somatic hypermutation during B-cell development [J]. Immunological Reviews.1987,96(1): 5-22.
[157]Ahmed R. and Gray D. Immunological memory and protective immunity: understanding their relation [J]. Science.1996,272(5258):54-60.
[158]Yao X., Liu Y. and Lin G. M. Evolutionary programming made faster [J]. IEEE Transactions on Evolutionary Computation.1999,3(2):82-101.
[159]Torn A. and Zilinskas A. Global optimization [J]. Lecture Notes in Computer Science. 1989,350:1-250.
[160]Han K. H. and Kim J. H. Quantum-inspired evolutionary algorithms with a new termination criterion, gate, and two-phase scheme [J]. IEEE Transaction on Evolutionary Computation.2004,8(2):156-169.
[161]Simon D. Biogeography-based optimization [J]. IEEE Transaction on Evolutionary Computation.2008,12(6):702-713.
[162]He S., Wu Q. H. and Saunders J. R. Group search optimizer:an optimization algorithm inspired by animal searching behavior [J]. IEEE Transaction on Evolutionary Computation.2009,13(5):973-990.
[163]Muller S. D. Marchetto J. Airaghi S. and Kournoutsakos P. Optimization based on bacterial chemotaxis [J]. IEEE Transaction on Evolutionary Computation.2002,6(1): 16-29.
[164]Wolpert D. H. and Macready W. G. No free lunch theorems for optimization [J]. IEEE Transaction on Evolutionary Computation.1997,1(1):67-82.
[165]Oyama E. and Tachi S. Modular neural net system for inverse kinematics learning [A]. Proceeding of the IEEE International Conference on Robotics and Automation [C]. 2000,3239-3246.