Abstract:In the mobile robot path planning problem in static multi-obstacle environment, Particle Swarm Optimization (PSO) algorithm has the disadvantages of easy premature convergence and poor local optimization ability, resulting in low accuracy of robot path planning. To solve the problem, a Multi-Objective Grasshopper Optimization Algorithm (MOGOA) was proposed. The path length, smoothness and security were taken as path optimization targets according to the mobile robot path planning requirements, and the corresponding mathematical model of multi-objective optimization problem was established. In the process of population search, the curve adaptive strategy was introduced to speed up the convergence of the algorithm, and the Pareto optimal criterion was used to solve the coexistence problem of the above three targets. Experimental results show that the proposed algorithm finds shorter paths and shows better convergence while solving the above problems. Compared with the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the proposed algorithm has the path length reduced by about 2.01 percentage, and the number of iterations reduced by about 19.34 percentage.
[1] 韩明, 刘教民, 吴朔媚, 等. 粒子群优化的移动机器人路径规划算法[J]. 计算机应用, 2017, 37(8):2258-2263. (HAN M, LIU J M, WU S M, et al. Path planning algorithm of mobile robot based on particle swarm optimization[J]. Journal of Computer Applications, 2017, 37(8):2258-2263.) [2] LI G, CHOU W. Path planning for mobile robot using self-adaptive learning particle swarm optimization[J]. Science China Information Sciences, 2018, 61(5):052204. [3] 贾会群, 魏仲慧, 何昕, 等.基于改进粒子群算法的路径规划[J]. 农业机械学报, 2018, 49(12):371-377. (JIA H Q, WEI Z H, HE X, et al. Path planning based on improved particle swarm optimization[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(12):371-377.) [4] 杨景明, 侯新培, 崔慧慧, 等.多策略改进的多目标粒子群优化算法[J]. 控制与决策, 2017, 32(3):435-442. (YANG J M, HOU X P, CUI H H, et al. Improved multi-objective particle swarm optimization algorithm based on integrating multiply strategies[J]. Control and Decision, 2017, 32(3):435-442.) [5] YU J, LAVALLE S M. Optimal multirobot path planning on graphs:complete algorithms and effective heuristics[J]. IEEE Transactions on Robotics, 2016, 32(5):1163-1177. [6] SAREMI S, MIRJALILI S, LEWIS A. Grasshopper optimisation algorithm:theory and application[J]. Advances in Engineering Software, 2017, 105:30-47. [7] XU Z, DU L, WANG H, et al. Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control[J]. Applied Mathematics and Mechanics (English Edition), 2019, 40(1):113-126. [8] TUMULURU P, RAVI B. GOA-based DBN:grasshopper optimization algorithm-based deep belief neural networks for cancer classification[J]. International Journal of Applied Engineering Research, 2017, 12(24):14218-14231. [9] BARMAN M, DEV CHOUDHURY N B, SUTRADHAR S. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India[J]. Energy, 2018, 145:710-720. [10] MIRJALILI S Z, MIRJALILI S, SAREMI S, et al. Grasshopper optimization algorithm for multi-objective optimization problems[J]. Applied Intelligence, 2018, 48(4):8005-820. [11] 闫旭, 叶春明.混合蝗虫优化算法求解作业车间调度问题[J]. 计算机工程与应用, 2019, 55(6):257-264. (YAN X, YE C M. Hybrid grasshopper optimization algorithm solves job-shop scheduling problem[J]. Computer Engineering and Applications 2019, 55(6):257-264.) [12] 程泽新, 李东生, 高杨.基于蝗虫算法的无人机三维航迹规划[J]. 飞行力学, 2019, 37(2):46-50, 55. (CHENG Z X, LI D S, GAO Y. UAV three-dimensional path planning based on grasshopper algorithm[J]. Flight Dynamics, 2019, 37(2):46-50, 55.) [13] HUA Y, JIN Y, HAO K. A clustering-based adaptive evolutionary algorithm for multiobjective optimization with irregular Pareto fronts[J]. IEEE Transactions on Cybernetics, 2019, 49(7):2758-2770. [14] THABIT S, MOHADES A. Multi-robot path planning based on multi-objective particle swarm optimization[J]. IEEE Access, 2018, 7:2138-2147. [15] MAC T T, COPOT C, TRAN D T, et al. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization[J]. Applied Soft Computing, 2017, 59:68-76. [16] COELLO C A C, LAMONT G B, van VELDHUIZEN D A. Evolutionary algorithms for solving multi-objective problems[M]. Boston:Springer, 2002. [17] SHIM M, SUH M, FURUKAWA T, et al. Pareto-based continuous evolutionary algorithms for multiobjective optimization[J]. Engineering Computations, 2002, 19(1):22-48. [18] FENG W, ZHANG L, YANG S, et al. A multiobjective evolutionary algorithm based on coordinate transformation[J]. IEEE Transactions on Cybernetics, 2019, 49(7):2732-2743. [19] DI L, ZHENG Z, XIA M. Robot path planning based on an improved multi-objective PSO method[C]//Proceedings of the 2016 International Conferenceon Computer Engineering, Information Science & Application Technology. Paris:Atlantis Press, 2016:496-501.