階層的時間論理仕様下でのマルチロボットのための分解ベース階層的タスク割当と計画
Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
複雑な時間論理仕様を階層的に分解し、サブタスク間の時間関係を考慮したタスクネットワークを構築して、混合整数線形計画でマルチロボットに割り当てる計画手法を提案した。
著者: Xusheng Luo, Shaojun Xu, Ruixuan Liu, Changliu Liu
分類: cs.RO, cs.AI
原文アブストラクト
Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on a single formula for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. A recent development has been the hierarchical representation of LTL~\cite{luo2024simultaneous} that contains multiple temporal logic specifications, providing a more interpretable framework. However, the proposed planning algorithm assumes the independence of robots within each specification, limiting their application to multi-robot coordination with complex temporal constraints. In this work, we formulated a decomposition-based hierarchical framework. At the high level, each specification is first decomposed into a set of atomic sub-tasks. We further infer the temporal relations among the sub-tasks of different specifications to construct a task network. Subsequently, a Mixed Integer Linear Program is used to assign sub-tasks to various robots. At the lower level, domain-specific controllers are employed to execute sub-tasks. Our approach was experimentally applied to domains of navigation and manipulation. The simulation demonstrated that our approach can find better solutions using less runtimes.
