Decentralized Task and Path Planning for Multi-Robot Systems
This work provides a decentralized planning framework for multi-robot systems, which is important for applications requiring robust and scalable coordination without a central authority.
This paper addresses decentralized task and path planning for multi-robot systems with tasks emerging over time. It proposes a framework that models tasks as MDPs/MOMDPs and uses a factor graph with the max-sum algorithm for decentralized task allocation, while a localized forward dynamic programming scheme handles collision avoidance.
We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) framework consisting of a task allocation module and a localized path planning module. Each task is modeled as a Markov Decision Process (MDP) or a Mixed Observed Markov Decision Process (MOMDP) depending on whether full states or partial states are observable. The task allocation module then aims at maximizing the expected pure reward (reward minus cost) of the robotic team. We fuse the Markov model into a factor graph formulation so that the task allocation can be decentrally solved using the max-sum algorithm. Each robot agent follows the optimal policy synthesized for the Markov model and we propose a localized forward dynamic programming scheme that resolves conflicts between agents and avoids collisions. The proposed framework is demonstrated with high fidelity ROS simulations and experiments with multiple ground robots.