基于改进PSO的四轮移动机器人全局路径规划  

Global Path Planning of Four-Wheel Mobile Robot Based on Improved PSO

作　　者：罗阳阳 彭晓燕[1] LUO Yang-yang;PENG Xiao-yan(College of Mechanical and Vehicle Engineering,Hunan University,Changsha Hunan 410082,China)

机构地区：[1]湖南大学机械与运载工程学院,湖南长沙410082

出　　处：《计算机仿真》2020年第7期373-379,共7页Computer Simulation

基　　金：国家自然科学基金(51575167,61573282,61603130)。

摘　　要：针对粒子群优化(PSO)算法在全局路径规划时存在收敛速度快但容易陷入局部最优的问题,提出了一种改进的PSO算法。利用随迭代次数递变的突变算子更新粒子的位置,在迭代初期,在小范围突变搜索更新粒子的位置,使得算法快速收敛。在迭代后期,在全局范围突变搜索更新粒子的位置,挣脱局部最优,寻找全局最优。并且将移动机器人运动学约束考虑到粒子位置更新中,使得机器人能够保证一定的速度跟踪所有规划的路径点。对比了改进前后的PSO算法在全局路径规划上的性能表现,结果表明改进的PSO算法在全局搜索能力和算法收敛速度上都优于改进前的算法。最后,将改进的PSO算法应用于一款移动机器人D1,通过实验证明了算法的有效性。For solving the problem that particle swarm optimization(PSO)algorithm has a fast convergence rate but is easy to fall into local optimum in global path planning,an improved PSO algorithm is proposed.The position of the particle was updated with a mutation operator that varies with the number of iterations.At the initial stage of the iteration,updating the position of particles by small-range mutation search can make the algorithm converge quickly.At the latter stage of the iteration,the position of the particle was updated in the global scope,and the local optimum was freed,and the global optimum was found.The kinematic constraints of mobile robot was considered when the particle position was updated,such that robots can follow the whole path.The performances of the improved PSO algorithm and traditional one on global path planning were compared.The results show that the improved PSO algorithm is superior to the pre-improvement in global search ability and convergence speed.Finally,the improved PSO algorithm was applied to a mobile robot named D1,which illustrates the effectiveness of the algorithm through experiments.

关 键 词：移动机器人 路径规划 粒子群算法 突变搜索 机器人运动学 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]