Sampling-based Minimum Risk path planning in multiobjective configuration spaces
Sampling-based Minimum Risk path planning in multiobjective configuration spaces
复制标题
多目标配置空间中基于采样的最小风险路径规划
DOI：
10.1109/cdc.2015.7402330
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Brendan Englot
中科院分区：
文献类型：
--
作者：
Tixiao Shan;Brendan Englot
我们提出了一种新的基于采样的路径规划算法，最优最小风险快速探索随机树（MR-RRT*），计划根据主要和次要成本标准的最小风险路径。主要成本标准是用户定义的累积风险度量，其可以表示接近障碍物、暴露于威胁或类似物。风险仅在配置空间中超过用户定义的阈值的区域中受到惩罚，导致许多图节点实现相同的主成本。该算法使用次要成本标准来打破主要成本的联系。所提出的方法为用户提供了灵活性，调整的相对重要性的替代成本标准，同时坚持渐进最优规划的要求，相对于主要成本。该算法的性能进行了比较与T-RRT*，另一种最优的可调风险规划算法，在一系列的计算实例与不同的风险表示。
We propose a new sampling-based path planning algorithm, the Optimal Minimum Risk Rapidly Exploring Random Tree (MR-RRT*), that plans minimum risk paths in accordance with primary and secondary cost criteria. The primary cost criterion is a user-defined measure of accumulated risk, which may represent proximity to obstacles, exposure to threats, or similar. Risk is only penalized in areas of the configuration space where it exceeds a user-defined threshold, causing many graph nodes to achieve identical primary cost. The algorithm uses a secondary cost criterion to break ties in primary cost. The proposed method affords the user the flexibility to tune the relative importance of the alternate cost criteria, while adhering to the requirements for asymptotically optimal planning with respect to the primary cost. The algorithm's performance is compared with T-RRT*, another optimal tunable-risk planning algorithm, in a series of computational examples with different representations of risk.