将多机器人规划策略抽象为可复用的超图，提升长期效率
Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs
- 用超图建模多机器人任务规划，支持高效搜索与分解
- 从过往经验中自动提取通用规划策略，实现跨任务复用
- 适合长期运行的多机器人系统，尤其擅长复杂任务协同
多机器人任务规划（MR-TP）是为一组机器人寻找完成任务的离散动作序列。该问题的复杂度随机器人数量和任务复杂度呈指数级增长，难以在线求解。为加速系统生命周期内的MR-TP，本文结合两项近期进展：(i) 可分解状态空间超图（DaSH），一种基于超图的新型框架，可高效建模与求解MR-TP问题；(ii) 学习-抽象（learning-by-abstraction），一种从单次规划经验中自动提取可泛化规划策略的技术，以供后续复用。本文重点拓展该策略学习方法，使其适用于基于超图的多机器人规划场景。
原文摘要 · Abstract (English)
Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging for online solution. To accelerate MR-TP over a system's lifetime, this work looks at combining two recent advances: (i) Decomposable State Space Hypergraph (DaSH), a novel hypergraph-based framework to efficiently model and solve MR-TP problems; and \mbox{(ii) learning-by-abstraction,} a technique that enables automatic extraction of generalizable planning strategies from individual planning experiences for later reuse. Specifically, we wish to extend this strategy-learning technique, originally designed for single-robot planning, to benefit multi-robot planning using hypergraph-based MR-TP.
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