NDLI: End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System

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End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System

Content Provider	MDPI
Author	Elfakharany, Ahmed Ismail, Zool
Copyright Year	2021
Description	In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot’s steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index ( $TAI$ ), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots.
Starting Page	2895
e-ISSN	20763417
DOI	10.3390/app11072895
Journal	Applied Sciences
Issue Number	7
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2021-03-24
Access Restriction	Open
Subject Keyword	Applied Sciences Robotics Deep Reinforcement Learning Task Allocation Navigation Multi-robot Systems
Content Type	Text
Resource Type	Article

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