基于蚁群和改进PRM算法的多目标点路径规划

Multiple Targets Robot Path Planning Based on Ant Colony and Improved Probabilistic Road Map

针对移动机器人最短路径问题,设计了一种包含了蚁群算法和改进PRM算法的融合算法.首先,根据实际地图建立了栅格化地图模型,并将地图模型用瘦化方法优化,然后,用蚁群算法排出其优先级,用改进的PRM算法进行路径规划,最后,给出了基于实际地图多目标点的仿真路径以及与同类算法的结果对比,改进算法比Bug2算法和D*算法快约2s,验证了蚁群算法和改进PRM算法融合的有效性.

Aiming at shortest path of robot path planning, this paper designs an ant colony algorithm （ACA） combine with improved probabilistic road map（PRM） method of moving robots in real map, which need to pass by several work points and back to starting point in a shortest way. Firstly, building a model of grid of work space and diminish it, then work out a priority level of work points, next using improved PRM method to plan a path, and finally gives the results of simulation based on real map, and results compared with other algorithm to show the reliability of this fusion algorithm. Improved algorithm is faster than Bug2 and D＊ method for 2 seconds, which proved the effectiveness of it.