多目标机械臂重排规划框架，高效处理复杂狭窄环境中的物品摆放顺序。
MO-SeGMan: Rearrangement Planning Framework for Multi Objective Sequential and Guided Manipulation in Constrained Environments
- 基于懒惰评估生成最优摆放序列，减少重规划与移动距离。
- 在9个基准任务中全部成功生成可行方案，速度与质量优于基线。
- 适合需要精准依赖关系维护的复杂重排场景，如仓储机器人。
本文提出MO-SeGMan，一种面向高度受限环境的多目标顺序引导重排规划框架。该方法通过懒惰评估机制生成物体放置序列，在最小化每个物体的重规划次数和机器人移动距离的同时，保留关键依赖结构。针对高度杂乱、非单调的场景，提出选择性引导前向搜索（SGFS），仅对关键障碍物进行有效重定位至可行位置。此外，采用自适应子目标选择优化方法，消除不必要的抓取-放置动作，提升整体解决方案质量。在9个基准重排任务上的大量实验表明，MO-SeGMan在所有情况下均能生成可行运动规划，解决方案时间更短，性能显著优于现有基线。结果验证了该框架在复杂重排问题中的鲁棒性与可扩展性。
原文摘要 · Abstract (English)
In this work, we introduce MO-SeGMan, a Multi-Objective Sequential and Guided Manipulation planner for highly constrained rearrangement problems. MO-SeGMan generates object placement sequences that minimize both replanning per object and robot travel distance while preserving critical dependency structures with a lazy evaluation method. To address highly cluttered, non-monotone scenarios, we propose a Selective Guided Forward Search (SGFS) that efficiently relocates only critical obstacles and to feasible relocation points. Furthermore, we adopt a refinement method for adaptive subgoal selection to eliminate unnecessary pick-and-place actions, thereby improving overall solution quality. Extensive evaluations on nine benchmark rearrangement tasks demonstrate that MO-SeGMan generates feasible motion plans in all cases, consistently achieving faster solution times and superior solution quality compared to the baselines. These results highlight the robustness and scalability of the proposed framework for complex rearrangement planning problems.
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