DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in
Complex Environments
- URL: http://arxiv.org/abs/2209.02865v1
- Date: Wed, 7 Sep 2022 00:35:27 GMT
- Title: DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in
Complex Environments
- Authors: Aakriti Agrawal, Senthil Hariharan, Amrit Singh Bedi, Dinesh Manocha
- Abstract summary: We present a novel reinforcement learning based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments.
We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it.
We observe improvement up to 14% in terms of task completion time and up-to 40% improvement in terms of computing collision-free trajectories for the robots.
- Score: 55.204450019073036
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a novel reinforcement learning (RL) based task allocation and
decentralized navigation algorithm for mobile robots in warehouse environments.
Our approach is designed for scenarios in which multiple robots are used to
perform various pick up and delivery tasks. We consider the problem of joint
decentralized task allocation and navigation and present a two level approach
to solve it. At the higher level, we solve the task allocation by formulating
it in terms of Markov Decision Processes and choosing the appropriate rewards
to minimize the Total Travel Delay (TTD). At the lower level, we use a
decentralized navigation scheme based on ORCA that enables each robot to
perform these tasks in an independent manner, and avoid collisions with other
robots and dynamic obstacles. We combine these lower and upper levels by
defining rewards for the higher level as the feedback from the lower level
navigation algorithm. We perform extensive evaluation in complex warehouse
layouts with large number of agents and highlight the benefits over
state-of-the-art algorithms based on myopic pickup distance minimization and
regret-based task selection. We observe improvement up to 14% in terms of task
completion time and up-to 40% improvement in terms of computing collision-free
trajectories for the robots.
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