当前位置: X-MOL 学术arXiv.cs.RO › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multi-Robot Formation Control Using Reinforcement Learning
arXiv - CS - Robotics Pub Date : 2020-01-13 , DOI: arxiv-2001.04527
Abhay Rawat, Kamalakar Karlapalem

In this paper, we present a machine learning approach to move a group of robots in a formation. We model the problem as a multi-agent reinforcement learning problem. Our aim is to design a control policy for maintaining a desired formation among a number of agents (robots) while moving towards a desired goal. This is achieved by training our agents to track two agents of the group and maintain the formation with respect to those agents. We consider all agents to be homogeneous and model them as unicycle [1]. In contrast to the leader-follower approach, where each agent has an independent goal, our approach aims to train the agents to be cooperative and work towards the common goal. Our motivation to use this method is to make a fully decentralized multi-agent formation system and scalable for a number of agents.

中文翻译:

使用强化学习的多机器人编队控制

在本文中,我们提出了一种机器学习方法来移动一组机器人。我们将问题建模为多智能体强化学习问题。我们的目标是设计一种控制策略,以在朝着预期目标前进的同时,在多个代理(机器人)之间维持所需的编队。这是通过训练我们的代理跟踪组中的两个代理并保持与这些代理相关的编队来实现的。我们认为所有代理都是同质的,并将它们建模为独轮车 [1]。与领导者 - 追随者方法相反,每个代理都有一个独立的目标,我们的方法旨在训练代理合作并朝着共同目标努力。我们使用这种方法的动机是建立一个完全去中心化的多代理形成系统,并且可以针对多个代理进行扩展。
更新日期:2020-01-15
down
wechat
bug