Learning decentralized control policies for multi-robot formation

Jiang Chao, Chen Zhuo, Guo Yi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Decentralized formation control has been extensively studied using model-based methods, which rely on model accuracy and communication reliability. Motivated by recent advances in deep learning techniques whereby an intelligent agent is trained to compute its actions directly from highdimensional raw sensory inputs using end-to-end decisionmaking policies, we consider the problem of learning decentralized control policies for multi-robot formation. A deep neural network is designed to model the control policy that maps the robot's local observations to control commands. We propose to use a centralized training framework based on supervised learning for control policy learning. The learned policy is then deployed on each robot in a decentralized manner for online formation control. Our proposed approach is verified and evaluated in experiments using a robotic simulator. Simulation results demonstrate satisfactory performance of formation control. Compared with existing methods for formation control, the proposed approach does not need inter-robot communication, and avoids hand-engineering the components of perception and control separately.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
Pages758-765
Number of pages8
ISBN (Electronic)9781728124933
DOIs
StatePublished - Jul 2019
Event2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019 - Hong Kong, China
Duration: 8 Jul 201912 Jul 2019

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2019-July

Conference

Conference2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
Country/TerritoryChina
CityHong Kong
Period8/07/1912/07/19

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