任务和运动信息树（TMIT *）：几乎必然渐近最优的集成任务和运动规划
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
摘要：
高水平的自主性需要离散和连续的推理来决定采取什么行动以及如何执行这些行动。集成任务和运动规划（TMP）算法联合解决这些混合问题以考虑离散符号动作之间的约束（即，任务计划）和它们的连续几何实现（即，运动计划）。这种联合的方法解决了更困难的问题比方法，解决任务和运动子问题独立。TMP算法结合并扩展了任务和运动规划的结果。TMP主要关注计算性能和完整性，较少关注解的最优性。最优TMP是困难的，因为独立的最优子问题可能不是最优的集成解决方案，这只能通过联合优化两个计划。本文提出了任务和运动信息树（TMIT*），一个最佳的TMP算法，结合的结果，从最大完工时间最优的任务规划和几乎必然渐近最优的运动规划。TMIT* 交错不对称的前向和逆向搜萦，以延迟计算昂贵的操作，直到必要的，并直接在问题的混合状态空间中执行有效的知情搜索。这使它能够快速解决问题，然后收敛到最佳的解决方案与额外的计算时间，所证明的评估机器人操作基准问题。
英文摘要：
High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms solve these hybrid problems jointly to consider constraints between the discrete symbolic actions (i.e., the task plan) and their continuous geometric realization (i.e., motion plans). This joint approach solves more difficult problems than approaches that address the task and motion subproblems independently. TMP algorithms combine and extend results from both task and motion planning. TMP has mainly focused on computational performance and completeness and less on solution optimality. Optimal TMP is difficult because the independent optima of the subproblems may not be the optimal integrated solution, which can only be found by jointly optimizing both plans. This paper presents Task and Motion Informed Trees (TMIT*), an optimal TMP algorithm that combines results from makespan-optimal task planning and almost-surely asymptotically optimal motion planning. TMIT* interleaves asymmetric forward and reverse searches to delay computationally expensive operations until necessary and perform an efficient informed search directly in the problem's hybrid state space. This allows it to solve problems quickly and then converge towards the optimal solution with additional computational time, as demonstrated on the evaluated robotic-manipulation benchmark problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
