基于一种改进PSO的移动机器人路径规划

提出了基于一种改进微粒群优化算法的移动机器人在已知环境信息下的路径规划方法。通过对算法中微粒的速度进化方式的改进，使算法能有效地对搜索空间进行搜索，避免陷入过早收敛，此外还将边界约束、静态避障和最短路径这3个条件表示成一个简单的适应度函数，使整个优化过程满足路径规划的任务要求。最后，通过仿真取得了很好的效果，证实了方法的可行性和有效性。

Path Planning for Mobile Robot Based on a Modified Particle Swarm Optimization

A path planning approach based on a modified particle swarm optimization (PSO) is presented for mobile robot in known static environments. To avoid premature convergence and to update candidate solutions more efficiently in the multidimensional search space, the best position of particle’s neighborhood is substituted for the individual optimized in the velocity relation of the algorithm. In addition, the requirements for boundary constraint, obstacle avoidance and shortest path are expressed as a simple fitness function to make the optimum solution fulfill the qualifications for path planning. Simulation results show that the proposed approach is feasible and effective.