未知环境下机器人路径规划的粒子群优化

研究了全局静态环境未知时机器人的路径规划问题,提出了一种新颖的基于粒子群算法的滚动规划算法。该方法在机器人视野域内产生若干个同心圆进行环境建模,然后利用粒子群优化算法规划出一条导航路径,机器人每前进一步,都由粒子群优化算法重新规划导航路径,因此,机器人前进路径不断动态修改,从而能使机器人沿一条全局优化的路径接近终点。仿真实验结果表明,即使在障碍物非常复杂的地理环境,用该算法也能迅速规划出一条优化路径,且能安全避碰,效果令人满意。

Particle swarm optimization for path planning of robot in unknown environment

The problem of path planning of mobile robot in an environment where the global information is unknown is studied，and a novel rolling planning algorithm based on particle swarm optimization is proposed.Firstly，a number of concentric circles are produced in the view of robot，and a navigation path is mapped out using particle swarm optimization.A navigation path will be gotten again using particle swarm optimization once the robot goes forward.So，the path for the robot is altered dynamically，which will make the robot move on a globally optimal path to the ending node.Simulation results indicate that the optimal path which the robot moves on can reach to the end safely and can be rapidly obtained even in complicated geographical environments，the effect being very satisfactory.