解决机器人在有限空间内重排物体的高效路径规划问题。
Tabletop Object Rearrangement: Structure, Complexity, and Efficient Combinatorial Search-Based Solutions
- 提出运行缓冲区大小理论，优化临时放置空间需求。
- 设计懒惰缓冲验证算法，提升多臂与移动机械臂效率。
- 适用于家庭、仓储等复杂场景的智能机器人重排任务。
本论文针对具有过手抓取能力的桌面物体重排（TORO）任务，深入分析其结构特性并提出高效算法解决方案。该任务在受限工作空间中重排多个物体面临双重挑战：一是动作序列规划以最小化拾放操作次数，属NP难问题；二是确定在杂乱环境中临时放置物体（即缓冲位姿）的位置，此过程至关重要却极为复杂。对于存在外部自由空间的TORO，本文研究了最小缓冲空间（即运行缓冲区大小），并给出理论分析与精确算法。对于无外部自由空间的情况，引入懒惰缓冲验证机制，并在单臂、双臂及移动机械臂等多种配置下评估其效率。
原文摘要 · Abstract (English)
This thesis provides an in-depth structural analysis and efficient algorithmic solutions for tabletop object rearrangement with overhand grasps (TORO), a foundational task in advancing intelligent robotic manipulation. Rearranging multiple objects in a confined workspace presents two primary challenges: sequencing actions to minimize pick-and-place operations - an NP-hard problem in TORO - and determining temporary object placements ("buffer poses") within a cluttered environment, which is essential yet highly complex. For TORO with available external free space, this work investigates the minimum buffer space, or "running buffer size," required for temporary relocations, presenting both theoretical insights and exact algorithms. For TORO without external free space, the concept of lazy buffer verification is introduced, with its efficiency evaluated across various manipulator configurations, including single-arm, dual-arm, and mobile manipulators.
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