Decentralized Task and Path Planning for Multi-Robot Systems

@article{Chen2021DecentralizedTA,
  title={Decentralized Task and Path Planning for Multi-Robot Systems},
  author={Yuxiao Chen and Ugo Rosolia and A. Ames},
  journal={IEEE Robotics and Automation Letters},
  year={2021},
  volume={6},
  pages={4337-4344}
}
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… 

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