Optimal Path Planning Based on a Multi-Tree T-RRT* Approach for Robotic Task Planning in Continuous Cost Spaces
Optimal Path Planning Based on a Multi-Tree T-RRT* Approach for Robotic Task Planning in Continuous Cost Spaces
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DOI：
10.1109/mecatronics.2018.8495886
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Cuebong Wong;Erfu Yang;Xiu T. Yan;Dongbing Gu
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文献类型：
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作者：
Cuebong Wong;Erfu Yang;Xiu T. Yan;Dongbing Gu
本文提出了一种在连续成本空间中进行机器人任务规划的集成方法。它由低级路径规划器和基于规划域定义语言 (PDDL) 的高级任务规划器组成。路径规划器基于最佳基于转换的快速探索随机树 (T-RRT*) 的多树实现，该树在环境中搜索所有配置航路点对之间的路径。还提出了一种基于代价函数的捷径方法。然后将所得的最小化路径成本传递给 PDDL 规划器，以解决高级任务规划问题，同时优化解决方案计划的总体成本。该方法在由不同成本函数组成的两种场景中进行了演示：杂乱环境中的障碍物清除和山区环境中的海拔。初步结果表明，与基于 T-RRT 的实现相比，可以在不显着增加计算时间的情况下实现路径质量的显着改进。
This paper presents an integrated approach to robotic task planning in continuous cost spaces. This consists of a low-level path planner and a high-level Planning Domain Definition Language (PDDL)-based task planner. The path planner is based on a multi-tree implementation of the optimal Transitionbased Rapidly-exploring Random Tree (T-RRT*) that searches the environment for paths between all pairs of configuration waypoints. A method for shortcutting paths based on cost function is also presented. The resulting minimized path costs are then passed to a PDDL planner to solve the high-level task planning problem while optimizing the overall cost of the solution plan. This approach is demonstrated on two scenarios consisting of different cost functions: obstacle clearance in a cluttered environment and elevation in a mountain environment. Preliminary results suggest that significant improvements to path quality can be achieved without significant increase to computation time when compared with a T-RRT-based implementation.