Distributed plan reconfiguration via knowledge transfer in multi-agent systems under local LTL specifications

We propose a cooperative motion and task planning scheme for multi-agent systems where the agents have independently-assigned local tasks, specified as Linear Temporal Logic (LTL) formulas. These tasks contain hard and soft sub-specifications. A least-violating initial plan is synthesized first for the potentially infeasible task and the partially-known workspace. While the system runs, each agent updates its knowledge about the workspace via its sensing capability and shares this knowledge with its neighboring agents. Based on this update, each agent verifies and revises its plan in real time. It is ensured that the hard specification is always fulfilled and the satisfaction for the soft specification is improved gradually. The design is distributed as only local interactions are assumed. The overall framework is demonstrated by a case study.