Cooperative Multi-Robot Navigation in Dynamic Environment with Deep Reinforcement Learning

Han, Ruihua; Chen, Shengduo; Hao, Qi

10.1109/ICRA40945.2020.9197209

2020

IEEE International Conference on Robotics and Automation (ICRA)

1050-4729

2577-087X

978-1-7281-7396-2

2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)

448-454

MAY 31-JUN 15, 2020

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRNP) under dynamic environments using a deep reinforcement learning (DRL) framework, which can help multiple robots jointly achieve optimal paths despite a certain degree of obstacle complexities. The novelty of this work includes threefold: (1) developing a cooperative architecture that robots can exchange information with each other to select the optimal target locations; (2) developing a DRL based framework which can learn a navigation policy to generate the optimal paths for multiple robots; (3) developing a training mechanism based on dynamics randomization which can make the policy generalized and achieve the maximum performance in the real world. The method is tested with Gazebo simulations and 4 differential drive robots. Both simulation and experiment results validate the superior performance of the proposed method in terms of success rate and travel time when compared with the other state-of-art technologies.

Collision avoidance Navigation Robot sensing systems Robot kinematics Training Adaptation models

第一 ; 通讯

英语

EI ; CPCI-S

National Natural Science Foundation of China[61773197]

Automation & Control Systems ; Engineering ; Robotics

Automation & Control Systems ; Engineering, Electrical & Electronic ; Robotics

WOS:000712319500052

20204309374941

Deep learning ; Multipurpose robots ; Industrial robots ; Air navigation ; Travel time

Air Transportation:431 ; Air Navigation and Traffic Control:431.5 ; Highway Transportation:432 ; Railroad Transportation:433 ; Waterway Transportation:434 ; Ergonomics and Human Factors Engineering:461.4 ; Artificial Intelligence:723.4 ; Robotics:731.5 ; Robot Applications:731.6

Web of Science

Southern Univ Sci & Technol, Dept Comp Sci & Engn, SUSTech Hayl Ctr Intelligent Transportat, Shenzhen Res Inst Trustworthy Autonomous Syst, Shenzhen 518055, Guangdong, Peoples R China

Han, Ruihua,Chen, Shengduo,Hao, Qi. Cooperative Multi-Robot Navigation in Dynamic Environment with Deep Reinforcement Learning[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:448-454.