【摘要】 为解决传统概率路标图算法(probabilistic roadmap,PRM)学习阶段路线图R(N,E)中路线图边集E较为复杂和查询阶段生成的路径转折次数较多的问题,提出边集优化方法并引入道格拉斯-普克算法。在学习阶段,通过对随机点进行约束,减少路线图中集合E的大小,减少查询阶段的计算量。在查询阶段,通过对搜索到的无碰撞路径节点进行峰值节点提取,有效去除冗余节点。实例仿真结果表明,改进PRM算法比标准的PRM算法具有更高的求解效率和更少的路径节点数目。更多还原

【Abstract】 To solve the problem that the roadmap edge set E in the roadmap R(N,E) in the learning phase of the traditional probabilistic roadmap(PRM) algorithm is more complicated and the number of path turns generated in the query phase is large,the edge set optimization method was proposed and Douglas-Puck algorithm method was introduced.In the learning phase,random points which were constrained to reduce the size of the set E in the road map,thereby reducing the amount of calculation in the query phase.In the query phase,peak nodes were extracted from the searched collision-free path nodes,redundant nodes were effectively removed.The example simulation results show that the improved PRM algorithm has higher solving efficiency and fewer path nodes than the standard PRM algorithm.更多还原