Cooperative Control of Multi-robot System Using Mobile Agent for Multiple Source Localization

2017 ◽  
pp. 210-221
Author(s):  
Naoya Ishiwatari ◽  
Yasunobu Sumikawa ◽  
Munehiro Takimoto ◽  
Yasushi Kambayashi
Keyword(s):  
Source Localization ◽  
Mobile Agent ◽  
Cooperative Control ◽  
Multiple Source ◽  
Robot System ◽  
Multi Robot

Underwater acoustic source localization using a multi-robot system: the DAMPS project

2021 ◽  
Author(s):  
Benedetto Allotta ◽  
Gianluca Antonelli ◽  
Antonino Bongiovanni ◽  
Andrea Caiti ◽  
Riccardo Costanzi ◽  
...  
Keyword(s):  
Source Localization ◽  
Acoustic Source ◽  
Robot System ◽  
Underwater Acoustic ◽  
Acoustic Source Localization ◽  
Multi Robot

scholarly journals Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 546
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Yuan Liu ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Formation Control ◽  
Rapid Development ◽  
Gradient Algorithm ◽  
Robot System ◽  
Physical Relationship ◽  
Unmanned Surface Vehicles ◽  
Main Challenge ◽  
Control Scheme ◽  
Multi Robot

The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs’ cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader–follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.

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