论文标题
通过局部定向可见性加速RRT*
Accelerated RRT* By Local Directional Visibility
论文作者
论文摘要
RRT*是一种有效的基于采样的运动计划算法。但是，如果不占据可访问的环境信息的优势，基于抽样的算法通常会导致抽样失败，产生无用的节点和/或失败探索狭窄的段落。对于本文，为了更好地利用环境信息并进一步提高搜索效率，我们提出了一种新颖的方法来改善RRT*，以1）量化邻居重新布置障碍物配置的本地知识，以定向可见性，2）在搜索过程中收集环境信息，以及3）在第一个obstacle Nodes偏向于近距离nodes sefly of Firstace Nodes seard of the of the of-obstace node seard of the of the of。局部定向可见性（RRT* -LDV）提出的算法RRT*更好地利用了局部已知信息，并创新了加权采样策略。加速的RRT* -LDV在收敛率和找到狭窄段落的成功率上优于RRT*。还试验了高自由度的场景。
RRT* is an efficient sampling-based motion planning algorithm. However, without taking advantages of accessible environment information, sampling-based algorithms usually result in sampling failures, generate useless nodes, and/or fail in exploring narrow passages. For this paper, in order to better utilize environment information and further improve searching efficiency, we proposed a novel approach to improve RRT* by 1) quantifying local knowledge of the obstacle configurations during neighbour rewiring in terms of directional visibility, 2) collecting environment information during searching, and 3) changing the sampling strategy biasing toward near-obstacle nodes after the first solution found. The proposed algorithm RRT* by Local Directional Visibility (RRT*-LDV) better utilizes local known information and innovates a weighted sampling strategy. The accelerated RRT*-LDV outperforms RRT* in convergence rate and success rate of finding narrow passages. A high Degree-Of-Freedom scenario is also experimented.
