基于PSO算法的移动机器人路径规划
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摘要
路径规划是移动机器人研究的一个重要方向,它作为自主式移动机器人导航的基本环节之一,是按照某一性能指标搜索一条从起始状态到目标状态的最优或近似最优的无碰路径。智能算法在这一领域的应用正在引起人们的注意。而粒子群优化算法(Particle Swarm Optimization, PSO算法),这种源于鸟群和鱼群群体运动行为的研究,是一种新的群体智能优化算法。它的主要特点是原理简单、参数少、收敛速度较快,所需领域知识少。将该方法应用于移动机器人路径规划,是本文的研究重点。主要内容如下：
(1)系统详尽地介绍了移动机器人路径规划的定义和分类,讨论了移动机器人路径规划研究意义、国内外研究进展和基本研究方向；PSO算法基本原理、多种改进形式、及其应用情况。
(2)采用链接图建立机器人工作空间障碍物顶点模型,用Dijkstra算法求得一条从起始状态到目标状态的可行避碰次优路径。
(3)应用原始PSO算法对求得的可行避碰次优路径进行优化,以求得全局最优路径。通过仿真结果,指出了原始PSO算法在粒子数目较少、迭代次数较少的情况下存在成功率低、易陷入局部极小的问题。
(4)针对存在的问题,提出了基于惯性权重与位置限量相结合的改进PSO算法。通过仿真试验,该方法在粒子数目较少、迭代次数较少的情况下仍然可以取得较高的成功率、较好的寻优结果。说明该方法是高效可行的。
(5)由于PSO算法中存在随机变量,实际应用中如果对时间进行限制,那么必然要减少粒子数目和迭代次数。随之而来的就是造成算法成功率的下降。针对这一问题,提出了检验最优值算法,当最优值不符合设置条件,将再次执行算法。根据仿真结果,证明了该算法是切实有效的。
Path planning is an important field of mobile robot research, and as a basic part of mobile robot navigation, it aims to search for a non-collision path from the starting point to the target point according to an optimal or sub-optimal performance target. Intelligent computing has aroused extensive attention as a way of solving this problem. Particle swarm optimization (PSO) is a new swarm intelligent optimization algorithm, inspired by the swarm behavior of bird flock and fish school. Its peculiarities are simplicity in principle, less parameters, fast convergence and less professional knowledge needed. To apply the PSO algorithm to solve the path planning of mobile robot is the main task of this study. They are as follows:
(1) It introduces the definitions and classifications about path planning of the mobile robot in detail, discusses the signification, development and, research areas of mobile robots. It also indicates basic principles of PSO algorithm, improved algorithms and its application.
(2) The MALINK graph was used to describe the obstacle extreme points in the working space of the mobile robot. By means of Dijkstra algorithm a feasible non-collision suboptimal path can be obtained from a starting point to the target point.
(3) Apply the original PSO algorithm to optimize the feasible non-collision suboptimal path so as to obtain the global optimal path. After reviewing the simulation results, we can find the original PSO algorithm is subjected to low success rate or fall in a local minimum on the condition of small number of particles, small number of iterations.
(4) In view of the problems above-mentioned, the improved PSO algorithm based on combination of inertia weight and location limitation was proposed. Simulation results show that the improved algorithm can obtain high success rate and good optimized results on the condition of lesser particles and lesser iteration circles. This indicates that the improved algorithm is efficacious and valid.
(5) Due to the random variables in PSO algorithm, it has to reduce the number of particles and iteration number if limiting the computation time in practice. Then it might reduce the success rate of the algorithm. For this problem, an algorithm to check the optimized result was proposed and this algorithm might conduct a run again if the optimal result does not meet the given conditions. The simulation results show that the algorithm is valid.
(1)系统详尽地介绍了移动机器人路径规划的定义和分类,讨论了移动机器人路径规划研究意义、国内外研究进展和基本研究方向；PSO算法基本原理、多种改进形式、及其应用情况。
(2)采用链接图建立机器人工作空间障碍物顶点模型,用Dijkstra算法求得一条从起始状态到目标状态的可行避碰次优路径。
(3)应用原始PSO算法对求得的可行避碰次优路径进行优化,以求得全局最优路径。通过仿真结果,指出了原始PSO算法在粒子数目较少、迭代次数较少的情况下存在成功率低、易陷入局部极小的问题。
(4)针对存在的问题,提出了基于惯性权重与位置限量相结合的改进PSO算法。通过仿真试验,该方法在粒子数目较少、迭代次数较少的情况下仍然可以取得较高的成功率、较好的寻优结果。说明该方法是高效可行的。
(5)由于PSO算法中存在随机变量,实际应用中如果对时间进行限制,那么必然要减少粒子数目和迭代次数。随之而来的就是造成算法成功率的下降。针对这一问题,提出了检验最优值算法,当最优值不符合设置条件,将再次执行算法。根据仿真结果,证明了该算法是切实有效的。
Path planning is an important field of mobile robot research, and as a basic part of mobile robot navigation, it aims to search for a non-collision path from the starting point to the target point according to an optimal or sub-optimal performance target. Intelligent computing has aroused extensive attention as a way of solving this problem. Particle swarm optimization (PSO) is a new swarm intelligent optimization algorithm, inspired by the swarm behavior of bird flock and fish school. Its peculiarities are simplicity in principle, less parameters, fast convergence and less professional knowledge needed. To apply the PSO algorithm to solve the path planning of mobile robot is the main task of this study. They are as follows:
(1) It introduces the definitions and classifications about path planning of the mobile robot in detail, discusses the signification, development and, research areas of mobile robots. It also indicates basic principles of PSO algorithm, improved algorithms and its application.
(2) The MALINK graph was used to describe the obstacle extreme points in the working space of the mobile robot. By means of Dijkstra algorithm a feasible non-collision suboptimal path can be obtained from a starting point to the target point.
(3) Apply the original PSO algorithm to optimize the feasible non-collision suboptimal path so as to obtain the global optimal path. After reviewing the simulation results, we can find the original PSO algorithm is subjected to low success rate or fall in a local minimum on the condition of small number of particles, small number of iterations.
(4) In view of the problems above-mentioned, the improved PSO algorithm based on combination of inertia weight and location limitation was proposed. Simulation results show that the improved algorithm can obtain high success rate and good optimized results on the condition of lesser particles and lesser iteration circles. This indicates that the improved algorithm is efficacious and valid.
(5) Due to the random variables in PSO algorithm, it has to reduce the number of particles and iteration number if limiting the computation time in practice. Then it might reduce the success rate of the algorithm. For this problem, an algorithm to check the optimized result was proposed and this algorithm might conduct a run again if the optimal result does not meet the given conditions. The simulation results show that the algorithm is valid.
引文
1.刘涛,李海滨,段志信.基于人工力场的移动机器人路径规划研究[J],计算机仿真,2007,24(11)：144-146.
2.赵继光,李红辉.改进遗传算法及其在路径优化问题中的应用[J],中国科技信息,2007,(22)：315-316.
3.国海涛,朱庆保,司应涛.一种蚂蚁粒子群融合的机器人路径规划新算法[J],计算机工程与应用,2007,(31)：39-41.
4.于芳,基于动觉智能图式的仿人智能控制在移动机器人路径规划中的研究[D],重庆：重庆大学,2007.
5.国海涛,朱庆保,徐守江.基于栅格法的机器人路径规划快速搜索随机树算法[J],南京师范大学学报(工程技术版),2007,7(2)：58-61.
6. Kondo K. Motion planning with six degrees of freedom by multi-strategic bi-directional heuristic free-space enumeration[J], IEEE Trans. On Robotics and Automation,1991, 7(3):267-277.
7. Kavraki L E, Sveska P, Latombe J C, Overmars M H. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J],IEEE Trans.on Robotics and Automation,1999,15(4):653-669.
8. Chen P C, Hwang Y K, SANDROS. A dynamic graph search algorithm for motion planning[J],IEEE Trans.on Robotics and Automation,1998,14(3):390-430.
9. Ahuactzin J M, Gupta K K. The kinematic roadmap:a motion planning based global approach for inverse kinematics of redundant robots[J], IEEE Traps on Robotics and Automation,1999,15(4):653-669.
10.张志广,基于坦克式移动机器人定位和路径规划技术研究[D],长沙：国防科学技术大学,2005.
11. Reif J H, Sharir M. Motion planning in the presence of moving obstacles[J], The Eskenasy Institute of Computer Science, Tel-Aviv Univ,1985, Tech Rept:39-85.
12. Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[J], The International Journal Robotics Research,1986,5(1):90-98.
13.高丽宏.移动机器人的路径规划和避障控制[D],大连：大连理工大学,2002.
14.覃柯,孙茂相,孙昌志.动态环境下基于改进人工势场法的机器人运动规划[J],沈阳工业大学学报2004,5(26)：568-571.
15.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2005,2(27)：152-157.
16.高云峰,高海.复杂环境下基于势场原理的路径规划[J],机器人,2004,(2)：114-118.
17.张颖,吕恬生.基于模拟退火-人工势场法的足球机器人路径规划研究[J],机械科学与技术,2003,22(4)：547-555.
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22. Shaffer C A, Herb G M.A real-time robot arm collision avoidance system[J], IEEE Trans.on Robotics and Automation,1992,8(2):149-160.
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26. Seraji H, Boo B. Real-time collision avoidance for position-controlled manipulators[J], IEEE Trans. on Rohotic.s and Automation,1999,15(4):670-677.
27.席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划[J],自动化学报,2002,28(2)：161-175.
28. Kennedy J, Eberhart R C. Particle Swarm Optimization[J],A. Proc of the IEEE Int I Conf on Neura Networks C,1995,4:1942-1948.
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32. Zheng Y, Ma L, Zhang L. On the convergence analysis and parameter selection in particle swarm optimization[J], Proceedings of International Conference on Machine Learning and Cybernetics,2003,3(11):1802-1807.
33. Carlisle A, Dozier G. An off-the-shelf PSO[J], Proceedings of the Workshop on Particle Swam Optimization, Indianapolis, IN,2001,1-6.
34. Shi Y, Eberhart R C. A Modified Particle Swarm Optimizer[J], Proceedings of the 1998 Conference of Evolutionary Computation. Piscataway, NJ:IEEE Press,1998,(5):69-73.
35. Shi Y, Ebethart R C. Fuzzy Adaptive Particle Swarm Optimization. Proceedings of the 2001 Congress on Evolutionary Computation. Piscataway,NJ,IEEE Press,2001,101-106.
36. Shi Y, Ebethart R C. Empirical study of Particle swarm optimization[J], Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL, 1999,3:1945-1950.
37. Clerc M. The swarm and queen:towards a deterministic and adaptive particle swarm optimization[J], Proceedings of the IEEE Congress on Evolutionary Computation, 1999,3:1951-1957.
38. Angeline P J. Using Selection to Improve Particle Swarm Optimization[J], Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ, IEEE Press,1999, (5):84-89.
39. Van den, Bergh F. An Analysis of Particle Swarm Optimizers[J], Department of Computer Science, University of Pretoria, South Africa,2002,101-105.
40. Lovbjerg M, Rasmussen T K, Krink T. Hybrid Particle Swarm Optimizer with Breeding and Subpopulations[J], Third Genetic and Evolutionary Conference. Piscataway, NJ, IEEE Press,2001,2(7):1105-1109.
41. Suganthan P N. Particle swarm optimizer with neighborhood operator[J], Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Piscataway, NJ, 1999,3(7):1958-1962.
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3.国海涛,朱庆保,司应涛.一种蚂蚁粒子群融合的机器人路径规划新算法[J],计算机工程与应用,2007,(31)：39-41.
4.于芳,基于动觉智能图式的仿人智能控制在移动机器人路径规划中的研究[D],重庆：重庆大学,2007.
5.国海涛,朱庆保,徐守江.基于栅格法的机器人路径规划快速搜索随机树算法[J],南京师范大学学报(工程技术版),2007,7(2)：58-61.
6. Kondo K. Motion planning with six degrees of freedom by multi-strategic bi-directional heuristic free-space enumeration[J], IEEE Trans. On Robotics and Automation,1991, 7(3):267-277.
7. Kavraki L E, Sveska P, Latombe J C, Overmars M H. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J],IEEE Trans.on Robotics and Automation,1999,15(4):653-669.
8. Chen P C, Hwang Y K, SANDROS. A dynamic graph search algorithm for motion planning[J],IEEE Trans.on Robotics and Automation,1998,14(3):390-430.
9. Ahuactzin J M, Gupta K K. The kinematic roadmap:a motion planning based global approach for inverse kinematics of redundant robots[J], IEEE Traps on Robotics and Automation,1999,15(4):653-669.
10.张志广,基于坦克式移动机器人定位和路径规划技术研究[D],长沙：国防科学技术大学,2005.
11. Reif J H, Sharir M. Motion planning in the presence of moving obstacles[J], The Eskenasy Institute of Computer Science, Tel-Aviv Univ,1985, Tech Rept:39-85.
12. Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[J], The International Journal Robotics Research,1986,5(1):90-98.
13.高丽宏.移动机器人的路径规划和避障控制[D],大连：大连理工大学,2002.
14.覃柯,孙茂相,孙昌志.动态环境下基于改进人工势场法的机器人运动规划[J],沈阳工业大学学报2004,5(26)：568-571.
15.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2005,2(27)：152-157.
16.高云峰,高海.复杂环境下基于势场原理的路径规划[J],机器人,2004,(2)：114-118.
17.张颖,吕恬生.基于模拟退火-人工势场法的足球机器人路径规划研究[J],机械科学与技术,2003,22(4)：547-555.
18. Holland J H, Genetic. Algorithms and the optimal allocations of trails[J], SIAM J. Computing,1973,2(2):88-105.
19.孙增圻.机器人智能控制[M],山西：山西教育出版社,1995：433-450.
20. Kirkpatrick S,Gelatt C D,Vecchi M P. Optimization by simulated annealing[J],Science, 1983,(220):671-680.
21.李大生,刘欣,吴明华,周济.基于动力学约束的机器人无碰运动规划[M],机器人,1990,12(5)：14-19.
22. Shaffer C A, Herb G M.A real-time robot arm collision avoidance system[J], IEEE Trans.on Robotics and Automation,1992,8(2):149-160.
23. Fiorini P, Shiller Z. Motion planning in dynamic environments using the relative velocity paradigm[J], Proc, IEEE Int. ConJ. on Robotics and Automation. Atlanta, GA,1993,(2): 560-566.
24. Lavalle S M, Kuffner L J. Randomized kinodynamic planning[J], IEEE Int Corfon Robotics and Automation,1999,1:473-479.
25.孟庆浩,彭商贤,刘大伟.基于Q-M图启发式搜索的移动机器人全局路径规划[J],机器人,1998,20(4)：273-279.
26. Seraji H, Boo B. Real-time collision avoidance for position-controlled manipulators[J], IEEE Trans. on Rohotic.s and Automation,1999,15(4):670-677.
27.席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划[J],自动化学报,2002,28(2)：161-175.
28. Kennedy J, Eberhart R C. Particle Swarm Optimization[J],A. Proc of the IEEE Int I Conf on Neura Networks C,1995,4:1942-1948.
29. Shi Y, Ebethart R C. Selection in particle swarm optimization[J], Proceedings of the Seventh Annual Conference on Evolutionary Programming, New York,1998,591-600.
30.杨志鹏,朱丽莉,袁华.粒子群优化算法研究与发展[J],计算机工程与科学,2007,29(6)：61-64.
31. El-gallad A L, EI-Hawary M E, Sallam A A. A Enhancing the particle swarm optimizer via proper parameters selection[J], Canadian Conference on Electrical and Computer Engineering,2002,2:792-797.
32. Zheng Y, Ma L, Zhang L. On the convergence analysis and parameter selection in particle swarm optimization[J], Proceedings of International Conference on Machine Learning and Cybernetics,2003,3(11):1802-1807.
33. Carlisle A, Dozier G. An off-the-shelf PSO[J], Proceedings of the Workshop on Particle Swam Optimization, Indianapolis, IN,2001,1-6.
34. Shi Y, Eberhart R C. A Modified Particle Swarm Optimizer[J], Proceedings of the 1998 Conference of Evolutionary Computation. Piscataway, NJ:IEEE Press,1998,(5):69-73.
35. Shi Y, Ebethart R C. Fuzzy Adaptive Particle Swarm Optimization. Proceedings of the 2001 Congress on Evolutionary Computation. Piscataway,NJ,IEEE Press,2001,101-106.
36. Shi Y, Ebethart R C. Empirical study of Particle swarm optimization[J], Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL, 1999,3:1945-1950.
37. Clerc M. The swarm and queen:towards a deterministic and adaptive particle swarm optimization[J], Proceedings of the IEEE Congress on Evolutionary Computation, 1999,3:1951-1957.
38. Angeline P J. Using Selection to Improve Particle Swarm Optimization[J], Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ, IEEE Press,1999, (5):84-89.
39. Van den, Bergh F. An Analysis of Particle Swarm Optimizers[J], Department of Computer Science, University of Pretoria, South Africa,2002,101-105.
40. Lovbjerg M, Rasmussen T K, Krink T. Hybrid Particle Swarm Optimizer with Breeding and Subpopulations[J], Third Genetic and Evolutionary Conference. Piscataway, NJ, IEEE Press,2001,2(7):1105-1109.
41. Suganthan P N. Particle swarm optimizer with neighborhood operator[J], Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Piscataway, NJ, 1999,3(7):1958-1962.
42. Van den, Bergh F, Engelbrecht A P. Using neighborhoods with the guaranteed convergence PSO[J], Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Indianapolis Indiana, USA,2003,(4):235-242.
43. Kennedy J. Small worlds and mega-minds:effects of neighborhood topology on particle swarm performance[J], Proceedings of IEEE Congress on Evolutionary Computation (CEC 1999),Piscataway,NJ,1999,3:1931-1938.
44. Kennedy J, Mendes R. Population structure and particle swarm performance[J], Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii USA,2002,2:1671-1676.
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