Fast and Adaptive Multi-Agent Planning under Collaborative Temporal Logic Tasks via Poset Products  

作　　者：Zesen Liu Meng Guo Weimin Bao Zhongkui Li 

机构地区：[1]Deparment of Mechanics and Engineering Science,College of Engineering,Peking University,Bejing 0081 China [2]Science and Technology Commission of China Aerospace Science and Technology Corporation,Beijing 100048,China

出　　处：《Research》2024年第2期81-94,共14页研究（英文）

基　　金：supported by the National Key R&D Program of China under grants 2022ZD0116401 and 2022ZD0116400;the National Natural Science Foundation of China under grants U2241214, 62373008, 62203017, and T2121002.

摘　　要：Efficient coordination and planning is essential for large-scale multi-agent systems that collaborate in a shared dynamic environment. Heuristic search methods or learning-based approaches often lack the guarantee on correctness and performance. Moreover, when the collaborative tasks contain both spatial and temporal requirements, e.g., as linear temporal logic (LTL) formulas, formal methods provide a verifiable framework for task planning. However, since the planning complexity grows exponentially with the number of agents and the length of the task formula, existing studies are mostly limited to small artificial cases. To address this issue, a new planning paradigm is proposed in this work for system-wide temporal task formulas that are released online and continually. It avoids two common bottlenecks in the traditional methods, i.e., (a) the direct translation of the complete task formula to the associated B點hi automaton and (b) the synchronized product between the B點hi automaton and the transition models of all agents. Instead, an adaptive planning algorithm is proposed, which computes the product of relaxed partially ordered sets (R-posets) on-the-fly and assigns these subtasks to the agents subject to the ordering constraints. It is shown that the first valid plan can be derived with a polynomial time and memory complexity with respect to the system size and the formula length. Our method can take into account task formulas with a length of more than 400 and a fleet with more than 400 agents, while most existing methods fail at the formula length of 25 within a reasonable duration. The proposed method is validated on large fleets of service robots in both simulation and hardware experiments.

关 键 词：HARDWARE SERVICE system 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]