Path Planning for Manipulation Using Experience-Driven Random Trees
Path Planning for Manipulation Using Experience-Driven Random Trees
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DOI：
10.1109/lra.2021.3063063
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发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Kavraki, Lydia E.
中科院分区：
文献类型：
--
作者：
Pairet, Eric;Chamzas, Constantinos;Kavraki, Lydia E.
机器人系统可能会经常遇到类似的操纵规划问题，导致类似的运动计划。代替从头开始规划每个问题，优选的是利用先前计算的运动规划，即，经验，以方便规划。已经提出了不同的方法来利用新的任务实例的先验信息。然而，这些方法依赖于大量的经验，当没有一个与当前问题密切相关时，这些方法就会失败。因此，一个开放的挑战是将先前的经验概括为不一定与先前相似的任务实例的能力。这项工作解决了上述挑战，提出了经验是“可分解的”和“可延展的”的命题，即，体验的部分适合于相关地探索机器人任务空间的连通性，即使在非体验区域中。两个新的规划师从这个洞察力的结果：经验驱动的随机树（ERT）和它的双向版本ERTConnect。这些规划者采用基于树采样的策略，增量地提取和调制单个路径体验的部分，以组成有效的运动规划。我们展示了我们的方法的任务实例，显着不同于以往的经验，并与相关国家的最先进的经验为基础的规划。虽然他们的修复策略无法概括数十次经验的先验，但我们的计划者，只有一次经验，在成功率和计划时间方面都显着优于他们。我们的规划器在Open Motion Planning Library中实现并免费提供。
Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferable to leverage previously computed motion plans, i.e., experiences, to ease the planning. Different approaches have been proposed to exploit prior information on novel task instances. These methods, however, rely on a vast repertoire of experiences and fail when none relates closely to the current problem. Thus, an open challenge is the ability to generalise prior experiences to task instances that do not necessarily resemble the prior. This work tackles the above challenge with the proposition that experiences are "decomposable" and "malleable," i.e., parts of an experience are suitable to relevantly explore the connectivity of the robot-task space even in non-experienced regions. Two new planners result from this insight: experience-driven random trees (ERT) and its bi-directional version ERTConnect. These planners adopt a tree sampling-based strategy that incrementally extracts and modulates parts of a single path experience to compose a valid motion plan. We demonstrate our method on task instances that significantly differ from the prior experiences, and compare with related state-of-the-art experience-based planners. While their repairing strategies fail to generalise priors of tens of experiences, our planner, with a single experience, significantly outperforms them in both success rate and planning time. Our planners are implemented and freely available in the Open Motion Planning Library.