マルチロボットシステムのための分散型タスク・経路計画
Decentralized Task and Path Planning for Multi-Robot Systems
複数ロボットが時間とともに発生するタスクを分散的に割り当て、衝突を避けながら経路計画する枠組みを提案し、ROSシミュレーションと実機実験で検証した。
著者: Yuxiao Chen, Ugo Rosolia, Aaron D. Ames
分類: cs.RO, cs.MA
原文アブストラクト
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.
