Hierarchical multi-robot strategies synthesis and optimization under individual and collaborative temporal logic specifications

Abstract

This paper presents a hierarchical framework for multi-robot temporal logic task planning. We assume that each robot has its individual task specification and the robots have to jointly satisfy a global collaborative task specification, both described in finite linear temporal logic. To reduce the overall computational complexity, a central server firstly extracts and decomposes a collaborative task sequence from the automaton corresponding to the collaborative task specification, and allocates the subtasks in the sequence to robots. The robots then synthesize their initial execution strategies based on locally constructed product automatons, which integrate task requirements of the assigned collaborative tasks and their individual task specifications. Further, to reduce robots’ wait time in collaborations, we propose a distributed execution strategy adjusting mechanism to iteratively improve the time efficiency of robots. Finally, we prove the completeness of the proposed framework under assumptions, and analyze its time complexity and optimality. Extensive simulation results verify the scalability and optimization efficiency of the proposed method.