基于区域搜索粒子群算法的机器人路径规划

针对粒子群算法应用于移动机器人路径规划时存在的易早熟、易陷入局部最优等问题,提出一种基于区域搜索的自适 应粒子群(region search-adaptive particle swarm optimization algorithm,RS-APSO)路径规划方法。 首先,通过区域搜索算法对原始 地图进行预处理,减少地图中的无效信息。 其次,提出两种可变算子对惯性权重因子进行调节,对加速因子进行自适应改进,增 强算法不同时期的搜索能力,利用新的加速因子使粒子快速摆脱较差区域。 最后通过动态避障策略,使机器人可以安全规避移 动障碍物。 仿真结果表明,RS-APSO 算法相较于 PSO 算法,平均运行时间降低了 30. 3%,平均迭代次数降低了 43. 9%,在动态 环境中也能生成安全路径。

Robot path planning based on region search particle swarm optimization

Aiming at the problems of particle swarm optimization in mobile robot path planning, such as precocity and local optimum, a path planning method based on region search-adaptive particle swarm optimization algorithm (RS-APSO) is proposed. Firstly, the region search algorithm is used to preprocess the original map to reduce invalid information in the map. Secondly, two variable operators are proposed to adjust the inertia weight factor and improve the acceleration factor adaptively to enhance the search ability of the algorithm in different periods. The new acceleration factor is used to remove the bad region quickly from the particle. Finally, the robot can safely avoid moving obstacles through dynamic obstacle avoidance strategy. Simulation results show that compared with PSO algorithm, the average running time of RS-APSO algorithm is reduced by 30. 3%, the average number of iterations is reduced by 43. 9%, and can also generate safe path in dynamic environment.