基于PSO和滚动优化的不确定环境下移动机器人动态路径规划

针对全局环境未知且存在动态障碍物情况下的移动机器人路径规划问题,本文提出了一种结合粒子群算法(PSO)和滚动优化策略的动态路径规划方法。通过在一系列移动空间窗口中进行在线规划来充分利用机器人实时测得的局部环境信息,并用粒子群算法求解每一个移动窗口内的最优路径。为及时躲避动态障碍物,提出了一种适用于动态未知环境下的适应度函数。仿真试验表明,该方法克服了现有局部路径规划方法的高复杂性的缺点,算法操作简单、具有全局寻优能力、收敛速度快、鲁棒性好,可以满足机器人在复杂的未知动态环境下路径规划的实时性要求。

Dynamic Path Planning of Mobile Robots in Uncertain Environments Based on PSO and Receding Horizon Optimization

A new planner based on the combination of a particle swarm algorithm(PSO) and a receding horizon optimization is developed in this paper for the path planning of single mobile robot in a global uncertain environment with dynamic obstacles.The robot path is planned on-line in a series of receding spatial windows to make full use of the local environment information sensed by the robot,and the particle swarm algorithm is applied in each receding window to optimize the predicted robot path.A evaluation function suitable for uncertain environments is proposed to avoid dynamic obstacles in time.Simulation results indicate that the proposed method has many advantages including simple realization,global optimization,rapid convergence and good robustness,meeting real-time requirements of robot path planning in complex uncertain environments with dynamic obstacles.