论文标题
通过选择性致密化的快速规划路线图
Fast Planning Over Roadmaps via Selective Densification
论文作者
论文摘要
我们建议通过配置空间进行快速运动计划的选择性致密方法。我们通过迭代添加配置来创建一系列路线图。我们将这些路线图组织成层，并在层之间相同的配置之间添加边缘。我们使用最佳优先搜索找到了一条路径，并在我们提出的剩余计划时间估算的指导下。该估计更喜欢扩展节点更接近目标和稀疏层的节点。 我们介绍了使用我们提出的图形和启发式图的路径质量和最大节点深度的证明。我们还提出了将选择性致密化与双向RRT连接以及许多图形搜索方法进行比较的实验。在需要探索密集层的困难环境中，我们发现选择性致密化的速度比所有其他方法都更快。
We propose the Selective Densification method for fast motion planning through configuration space. We create a sequence of roadmaps by iteratively adding configurations. We organize these roadmaps into layers and add edges between identical configurations between layers. We find a path using best-first search, guided by our proposed estimate of remaining planning time. This estimate prefers to expand nodes closer to the goal and nodes on sparser layers. We present proofs of the path quality and maximum depth of nodes expanded using our proposed graph and heuristic. We also present experiments comparing Selective Densification to bidirectional RRT-connect, as well as many graph search approaches. In difficult environments that require exploration on the dense layers we find Selective Densification finds solutions faster than all other approaches.
