Robot Path Planning and Experiment with an Improved PSO Algorithm
KANG Yuxiang1, JIANG Chunying1, QIN Yunhai2, YE Changlong1
1. School of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China; 2. Suzhou Automobile Research Institute of Tsinghua University, Suzhou 215000, China
Abstract:An improved PSO (particle swarm optimization) algorithm is proposed for robot path planning, aiming at the shortcomings of PSO algorithm, such as the low optimization accuracy and the premature. Firstly, an improved model of particle velocity update is proposed according to the principle that the variables change in the direction of negative gradient in gradient descent method. Then, an adaptive particle position update coefficient is added in order to improve the efficiency and the accuracy of particle search. Finally, an improved PSO algorithm is designed by introducing ε-greedy strategy. The experimental results on some optimized test functions show that the search accuracy of the proposed algorithm is at least twice that of other algorithms, and the convergence speed is also greatly improved. The proposed algorithm and the improved DC-HPSO (dynamic clustering hybrid PSO) algorithm are applied to the simulations and the actual experiments of static path planning. Results show that the proposed model has the advantages of high accuracy, high efficiency and high success rate.
[1] 曲道奎,杜振军,徐殿国,等.移动机器人路径规划方法研究[J].机器人,2008,30(2):97-101,106.Qu D K, Du Z J, Xu D G, et al. Research on path planning for a mobile robot[J]. Robot, 2008, 30(2):97-101,106. [2] Dewang H S, Mohanty P K, Kundu S. A robust path planning for mobile robot using smart particle swarm optimization[J]. Procedia Computer Science, 2018, 133:290-297. [3] Mohanty P K, Parhi D R. Controlling the motion of an autonomous mobile robot using various techniques:A review[J]. Journal of Advance Mechanical Engineering, 2013, 10(1):24-39. [4] Mo H W, Xu L F. Research of biogeography particle swarm optimization for robot path planning[J]. Neurocomputing, 2015, 148:91-99. [5] Mac Thi T, Copot C, Tran D T, et al. Heuristic approaches in robot path planning:A survey[J]. Robotics and Autonomous Systems, 2016, 86:13-28. [6] 赵晓,王铮,黄程侃,等.基于改进A* 算法的移动机器人路径规划[J].机器人,2018,40(6):903-910.Zhao X, Wang Z, Huang C K, et al. Mobile robot path planning based on an improved A* algorithm[J]. Robot, 2018, 40(6):903-910. [7] 朱大奇,孙兵,李利.基于生物启发模型的AUV三维自主路径规划与安全避障算法[J].控制与决策,2015,30(5):798-806.Zhu D Q, Sun B, Li L. Algorithm for AUV's 3-D path planning and safe obstacle avoidance based on biological inspired model[J]. Control and Decision, 2015, 30(5):798-806. [8] He W, Chen Y H, Yin Z. Adaptive neural network control of an uncertain robot with full-state constraints[J]. IEEE Transactions on Cybernetics, 2016, 46(3):620-630. [9] Mohanty P K, Parhi D R. Navigation of autonomous mobile robot using adaptive network based fuzzy inference system[J]. Journal of Mechanical Science and Technology, 2014, 28(7):2861-2868. [10] 刘建华,杨建国,刘华平,等.基于势场蚁群算法的移动机器人全局路径规划方法[J].农业机械学报,2015,46(9):18-27.Liu J H, Yang J G, Liu H P, et al. Robot global path planning based on ant colony optimization with artificial potential field[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(9):18-27. [11] 刘二辉,姚锡凡.基于改进遗传算法的自动导引小车路径规划及其实现平台[J].计算机集成制造系统,2017,23(3):465-472.Liu E H, Yao X F. AGV path planning based on improved genetic algorithm and implementation platform[J]. Computer Integrated Manufacturing Systems, 2017, 23(3):465-472. [12] Mohanty P K, Parhi D R. A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach[J]. Memetic Computing, 2015, 7(4):255-273. [13] Kennedy J, Eberhart R. Particle swarm optimization[C]//IEEE International Conference on Neural Networks. Piscataway, USA:IEEE, 1995:1942-1948. [14] Kennedy J. Stereotyping:Improving particle swarm performance with cluster analysis[C]//IEEE Congress on Evolutionary Computation. Piscataway, USA:IEEE, 2000:1507-1512. [15] Li C H, Yang S X. A clustering particle swarm optimizer for dynamic optimization[C]//IEEE Congress on Evolutionary Computation. Piscataway, USA:IEEE, 2009:439-446. [16] Shi Y, Eberhart R C. Parameter selection in particle swarm optimization[M]//Lecture Notes in Computer Science, Vol.1447. Berlin, Germany:Springer-Verlag, 1998:591-600. [17] Saska M, Macas M, Preucil L, et al. Robot path planning using particle swarm optimization of Ferguson splines[C]//IEEE Conference on Emerging Technologies and Factory Automation. Piscataway, USA:IEEE, 2006:839-833. [18] Chien W, Chiu C C, Cheng Y T, et al. Multi-objective optimization for UWB antenna array by APSO algorithm[J]. Telecommunication Systems, 2017, 64(4):649-660. [19] Eberhart R C, Shi Y. Comparing inertia weights and constriction factors in Conference particle Swarm optimization[C]//IEEE Congress on Evolutionary Computation. Piscataway, USA:IEEE, 2000:84-88. [20] Shi Y, Eberhart R C. A modified particle swarm optimizer[C]//IEEE Congress on Evolutionary Computation. Piscataway, USA:IEEE, 1998:69-73. [21] Santos R, Borges G, Santos A, et al. A semi-autonomous particle swarm optimizer based on gradient information and diversity control for global optimization[J]. Applied Soft Computing, 2018, 69:330-343. [22] Behnamian J, Ghomi S M T F. Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting[J]. Expert Systems with Applica-tions, 2010, 37(2):974-984. [23] Das P K, Behera H S, Panigrahi B K. A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning[J]. Swarm and Evolutionary Computation, 2016, 28:14-28.