基于混合粒子群算法的移动机器人路径规划

为了确定复杂环境中移动机器人最优轨迹,提出了一种混合粒子群优化算法(IPSO-GOP).首先对粒子群优化算法进行改进,在算法运行的各个阶段对惯性权重进行自适应调整来增强粒子的搜索能力,并采用混沌变量对粒子进行扰动以提高收敛速度;其次,为了提高算法寻优能力,摆脱局部极小值并增加种群的多样性,引入遗传算法继承的多重交叉和变异两个进化算子(GOP)优化改进版本的粒子群算法(IPSO);最后,使用三次样条插值对该混合算法生成的路径进行平滑处理,得到无碰撞最短的几何连续路径.实验结果表明,多障碍物环境下IPSO-GOP算法减少了陷入局部最优的发生,加快了收敛速度;同时,与原粒子群优化算法(PSO)相比,该算法寻优能力显著,在路径规划问题上有明显的优势.

Path Planning for Mobile Robot Based on Improved Particle Swarm Optimization Algorithm

This study proposes an Improved Particle Swarm Optimization with Genetic OPerators (IPSO-GOP) to determine the optimal trajectory of mobile robots in a complex environment. Firstly, we improve the Particle Swarm Optimization (PSO) and adaptively adjust the inertia weight during the algorithm operation to facilitate the particle search. Besides, we disturb the particles with the chaotic variables to increase the convergence speed. Secondly, we introduce the Genetic OPerators (GOP), i.e., multi-crossover and mutation inherited by the genetic algorithm, to optimize the improved PSO (IPSO), thus getting rid of the local minimum and promoting the population diversity. Finally, the shortest continuous geometric path without collisions is obtained after cubic spline interpolation smooths the path generated by the proposed algorithm. In addition, the proposed algorithm in a multi-obstacle environment circumvents the local optimum and accelerates the convergence. Compared with the PSO, it has significant optimization and advantages in path planning.