Analysis and solution of a predator–protector–prey multi-robot system by a high-level reinforcement learning architecture and the adaptive systems theory

2010 ◽  
Vol 58 (12) ◽  
pp. 1266-1272 ◽  
Author(s):  
José Antonio Martín H. ◽  
Javier de Lope ◽  
Darío Maravall
Keyword(s):  
Reinforcement Learning ◽  
Systems Theory ◽  
Adaptive Systems ◽  
Robot System ◽  
High Level ◽  
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.

CBR Based Collision Avoidance Approach to Reactive Control for Multi-Robot System

2012 ◽  
Vol 232 ◽  
pp. 398-402
Author(s):  
Qiang Liu ◽  
Jia Chen Ma ◽  
Qi Zhang
Keyword(s):  
Collision Avoidance ◽  
Simulation Software ◽  
Case Based Reasoning ◽  
Reactive Control ◽  
Robot System ◽  
Dynamic Selection ◽  
On Line ◽  
High Level ◽  
On Line Learning ◽  
Multi Robot

In this paper we discuss a Case-Based Reasoning (CBR) method which is an on-line learning mechanism for dynamic selection and modification of behavior assemblages for collision avoidance of multi-robot system. The CBR module is designed as an additional reactive control system which provide flexible performance in novel environments without extensive high-level reasoning that would slow the system down. The results by robot simulation software MissionLab show that the CBR are effective for making decisions to avoid the collision with static obstacles as well as moving robots in multi-robot system.

scholarly journals DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)

2021 ◽  
Vol 3 (1) ◽  
pp. 69-98
Author(s):  
Paul Gautier ◽  
Johann Laurent
Keyword(s):  
Reinforcement Learning ◽  
Task Allocation ◽  
Computing System ◽  
Processing Load ◽  
Robot System ◽  
Research Challenges ◽  
Local Solutions ◽  
Overall Efficiency ◽  
Robot Task ◽  
Multi Robot

Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.

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