渐近最优的多机器人任务与运动规划

中文摘要 AI 辅助

多机器人任务与运动规划（MR-TAMP）需要联合推理多个交互机器人的离散任务决策和连续无碰撞运动。尽管已为任务与运动规划开发了渐近最优算法，但将这些保证扩展到多机器人设置中引入了一个重要挑战：不同的任务转换可能涉及不同的机器人子集，因此对复合配置空间施加不同维度的约束。因此，渐近最优的规划器不仅必须在每个任务模式内优化运动，还必须确保对连接它们的不同类型的转换进行充分探索。我们刻画了这种转换结构，并为MR-TAMP中的全局渐近最优性建立了充分条件，要求对相关转换进行持续覆盖，并在连接的可行区域内进行渐近改进的运动规划。基于这些条件，我们开发了一种高效的渐近最优MR-TAMP算法，该算法结合了演化的单机器人路标图与隐式张量积搜索，避免了显式构建复合路标图。该规划器进一步采用条件转换采样、惰性碰撞检测以及模式和解决方案级引导，以提高有限时间规划效率，同时保持持续探索。所得到的框架为多机器人操作提供了渐近最优性保证，同时有效利用了单机器人运动规划的结构。

英文摘要

Multi-robot task and motion planning (MR-TAMP) requires jointly reasoning about discrete task decisions and continuous collision-free motions of multiple interacting robots. Although asymptotically optimal algorithms have been developed for task and motion planning, extending these guarantees to the multi-robot setting introduces an important challenge: different task transitions may involve different subsets of robots and therefore impose constraints of different dimensions on the composite configuration space. Consequently, an asymptotically optimal planner must not only optimize motion within each task mode, but also ensure sufficient exploration of the different types of transitions connecting them. We characterize this transition structure and establish sufficient conditions for global asymptotic optimality in MR-TAMP, requiring persistent coverage of relevant transitions and asymptotically improving motion planning within connected feasible regions. Based on these conditions, we develop an efficient asymptotically optimal MR-TAMP algorithm that combines evolving individual-robot roadmaps with implicit tensor-product search, avoiding explicit construction of the composite roadmap. The planner further employs conditional transition sampling, lazy collision checking, and mode- and solution-level guidance to improve finite-time planning efficiency while retaining persistent exploration. The resulting framework provides asymptotic optimality guarantees for multi-robot manipulation while efficiently exploiting the structure of individual-robot motion planning.

发表机构

• Nanyang Technological University（南洋理工大学）
• The Australian National University（澳大利亚国立大学）

机构由 AI 辅助整理，请以论文原文为准。