Decentralized Markov Decision Processes for Handling Temporal and Resource constraints in a Multiple Robot System

Abstract

We consider in this paper a multi-robot planning system where robots realize a common mission with the following characteristics : the mission is an acyclic graph of tasks with dependencies and temporal window validity. Tasks are dis- tributed among robots which have uncertain durations and resource consumptions to achieve tasks. This class of problems can be solved by using decision-theoretic plan- ning techniques that are able to handle local temporal constraints and dependencies between robots allowing them to synchronize their processing. A specific decision model and a value function allow robots to coordinate their actions at runtime to maximize the overall value of the mission realization. For that, we design in this paper a cooperative multi-robot planning system using distributed Markov Decision Processes (MDPs) without communicating. Robots take uncertainty on temporal intervals and dependencies into consideration and use a distributed value function to coordinate the actions of robots.