Research on Path Planning Method of Particle Swarm Optimization Algorithm and Fusion Behavior Dynamics
-
摘要: 针对未知环境下采用竞争行为动力学协调方法进行移动机器人路径规划时存在机器人运动速度参数确定困难,并且在各基本行为竞争时容易产生行为参数振荡现象而导致规划出来的路径不优化等问题,提出了基于粒子群优化算法融合行为动力学进行路径规划的方法。该方法在行为动力学模型的基础上利用粒子群优化算法(Particle swarm optimization,PSO)对路径规划过程中的基本行为进行融合,代替了竞争行为动力学的行为参数协调,从而使移动机器人能够根据传感器采集的实时环境信息自动获取各个基本行为的权值。通过把该方法与基于竞争动力学行为协调方法的机器人路径规划进行了仿真对比实验,实验结果验证了该方法的可行性和优越性。Abstract: When using competitive behavior dynamics coordination methods to mobile robot path planning in unknown environment, the robot motion velocity parameters are difficult determined and it is easy to generate parameter oscillation in behavior competition, leading to the path of planning not optimization. A new path planning method based on particle swarm optimization for behavior dynamics algorithm is proposed. In this method, the particle swarm optimization algorithm is used to integrate the basic behaviors in path planning process based on the behavior dynamics model, which instead of the behavior parameter coordination of the competitive behavior dynamics. So that the robot can automatically obtain the weight of each basic behavior according to the real-time environment information collected by the vision senor. By comparing the proposed method with the competitive behavior dynamic coordination methods, the experiment results verify the feasibility and superiority of the proposed method.
-
Key words:
- mobile robot /
- path planning /
- behavior dynamics /
- particle swarm optimization /
- unknown environment
-
[1] Siegwart R, Nourbakhsh I R. Introduction to autonomous mobile robots[M]. Massachusetts:MIT Press, 2004 [2] 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 Zhu D Q, Yan M Z. Survey on technology of mobile robot path planning[J]. Control and Decision, 2010,25(7):961-967(in Chinese) [3] 李磊,叶涛,谭明,等.移动机器人技术研究现状与未来[J].机器人,2002,24(5):475-480 Li L, Ye T, Tan M, et al. Present state and future development of mobile robot technology research[J]. Robot, 2002,24(5):475-480(in Chinese) [4] 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 Zhang C G, Xi Y G. Robot rolling path planning based on locally detected information[J]. Acta Automatica Sinica, 2003,29(1):38-44(in Chinese) [5] Agirrebeitia J, Avilés R, de Bustos I F, et al. A new APF strategy for path planning in environments with obstacles[J]. Mechanism and Machine Theory, 2005,40(6):645-658 [6] Burchardt H, Salomon R. Implementation of path planning using genetic algorithms on mobile robots[C]//Proceedings of IEEE Congress on Evolutionary Computation. Vancouver:IEEE, 2006:1831-1836 [7] Abdessemed F, Benmahammed K, Monacelli E. A fuzzy-based reactive controller for a non-holonomic mobile robot[J]. Robotics and Autonomous Systems, 2004,47(1):31-46 [8] Shi X P, Liu S R, Liu F. New robust control strategy for module manipulators via sliding mode control with an extended state observer[J]. Proceedings of the Institution of Mechanical Engineers, Part I:Journal of Systems and Control Engineering, 2010,224(5):545-555 [9] Ge S S, Cui Y J. New potential functions for mobile robot path planning[J]. IEEE Transactions on Robotics and Automation, 2000,16(5):615-620 [10] Jiao L C, Li Y Y, Gong M G, et al. Quantum-inspired immune clonal algorithm for global optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008,38(5):1234-1253 [11] Sch·ner G, Dose M. A dynamical systems approach to task-level system integration used to plan and control autonomous vehicle motion[J]. Robotics and Autonomous Systems, 1992,10(4):253-267 [12] Lei Y M. The behavior fusion based on particle swarm optimization method[C]//Proceedings of IEEE International Conference on Computer, Mechatronics, Control and Electronic Engineering. Changchun, China:IEEE, 2010:380-383 [13] Bicho E, Mallet P, Sch·ner G. Using attractor dynamics to control autonomous vehicle motion[C]//Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society. Aachen, Germany:IEEE, 1998:1176-1181 [14] Reimann H, Iossifidis I, Sch·ner G. Integrating orientation constraints into the attractor dynamics approach for autonomous manipulation[C]//Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots. Nashville, TN:IEEE, 2010:294-301 [15] 郝大鹏,傅卫平,王雯.基于行为动力学的移动机器人安全导航方法[J].系统工程与电子技术,2014,36(1):136-142 Hao D P, Fu W P, Wang W. Mobile robot safe navigation based on behavior dynamics[J]. Systems Engineering and Electronics, 2014,36(1):136-142(in Chinese) [16] Hale J G, Hohl B, Hyon S H, et al. Highly precise dynamic simulation environment for humanoid robots[J]. Advanced Robotics, 2008,22(10):1075-1105 [17] Guo M Z, Liu Y, Malec J. A new Q-learning algorithm based on the metropolis criterion[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004,34(5):2140-2143 [18] Morimoto J, Atkeson C G. Learning biped locomotion[J]. IEEE Robotics & Automation Magazine, 2007,14(2):41-51
点击查看大图
计量
- 文章访问数: 149
- HTML全文浏览量: 35
- PDF下载量: 3
- 被引次数: 0