Decomposition of Finite LTL Specifications for Efficient Multi-agent Planning

Generating verifiably correct execution strategies from Linear Temporal Logic (LTL) mission specifications avoids the need for manually designed robot behaviors. However, when incorporating a team of robot agents, the additional model complexity becomes a critical issue. Given a single finite LTL mission and a team of robots, we propose an automata-based approach to automatically identify possible decompositions of the LTL specification into sets of independently executable task specifications. Our approach leads directly to the construction of a team model with significantly lower complexity than other representations constructed with conventional methods. Thus, it enables efficient search for an optimal decomposition and allocation of tasks to the robot agents.