粒子群算法改进灰狼算法的机器人路径规划

Robot Path Planning Based on Particle Swarm Optimization Improved Grey Wolf Algorithm

路径规划的目的是在有障碍物的环境中寻找可行性路径,即从起始位置到目标位置长度最小的无碰撞路径。针对避障路径规划问题,提出基于灰狼算法的改进方法,用于提高路径规划效率。改进算法将粒子群优化算法与灰狼算法相结合,融合二者思想改进灰狼算法的位置更新公式,通过粒子群算法改善灰狼算法易过早收敛而陷入局部最优以及收敛速度较低的问题。实验结果表明,改进算法在求取全局最优解和收敛速度方面相较于基础灰狼算法均有不同程度的提高,平均求解时间仅为基础算法的89.8%,迭代次数为基础算法的83%。粒子群优化算法改进的灰狼算法具有更加优秀的路径规划性能。

The path planning aims to find a feasible path in the environment with obstacles,that is,the collision free path with the minimum length from the starting position to the target position.In order to solve the problem of obstacle avoidance path planning,proposes an improved method of grey wolf algorithm(GWO)for the optimization of group intelligence,which is used to solve the problem of inefficient path planning.The algorithm combines particle swarm optimization(PSO)and wolf algorithm,combines the two algorithms to improve the position updating formula of grey wolf algorithm.The algorithm is easy to converge early and fall into the problem of local optimal and low convergence speed.The experimental results show that compared with the basic grey wolf algorithm,the improved algorithm has different degrees of improvement in global optimal solution and convergence speed.The average solution time is only 89.8%of the basic algorithm,and the number of iterations is only 83%.The improved algorithm has better performance in path planning.