Abstract 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.
In our previous work REF , we proposed an approach for automatically identifying all possible decomposition choices of a specification into independent tasks.
Decomposition of Finite LTL Specifications for Efficient Multi-agent Planning
DARS
Computer Science