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Solving a collision-aware multi-agent mission planning (task allocation andpath finding) problem is challenging due to the requirement of real-timecomputational performance, scalability, and capability of handlingstatic/dynamic obstacles and tasks in a cluttered environment. This paperproposes a distributed real-time (on the order of millisecond) algorithmDrMaMP, which partitions the entire unassigned task set into subsets viaapproximation and decomposes the original problem into several single-agentmission planning problems. This paper presents experiments with dynamicobstacles and tasks and conducts optimality and scalability comparisons with anexisting method, where DrMaMP outperforms the existing method in both indices.Finally, this paper analyzes the computational burden of DrMaMP which isconsistent with the observations from comparisons, and presents the optimalitygap in small-size problems.

DrMaMP: Distributed Real-time Multi-agent Mission Planning in Cluttered Environment