一种障碍环境下机器人路径规划的蚁群粒子群算法

Ant colony optimization and particle swarm optimization for robot-path planning in obstacle environment

针对机器人在障碍环境下寻找最优路径问题,提出了一种障碍环境下机器人路径规划的蚁群粒子群算法.该方法有效地结合了粒子群算法和蚁群算法的优点,采用栅格法进行环境建模,利用粒子群算法的快速简洁等特点得到蚁群算法初始信息素分布,以减少迭代次数,加快算法的收敛速度;同时利用蚁群算法之间的可并行性,采用分布式技术实现蚂蚁之间的并行搜索,求解精度高等优点,求精确解.仿真实验结果证明了该方法的有效性,是机器人路径规划的一种较好的方法.

For searching the best path for a robot in an obstacle environment, this paper proposes an algorithm of ant colony optimization（ACO） and particle swarm optimization（PSO） for path planning. The new algorithm effectively combines the advantages of ACO and PSO. It adopts the grid method for environment modeling and makes use of the efficiency and succinctness of PSO to obtain the initial distribution of pheromone, reducing the number of iterations and accelerating the convergence. At the same time, by using the parallelizability of ants and distributed parallelized-searching technology, the performance of the algorithm is effectively improved. The simulation result shows the effectiveness of the proposed algorithm in solving the problem of path planning.