Coverage Control in Multi-Robot Systems via Graph Neural Networks
Coverage Control in Multi-Robot Systems via Graph Neural Networks
复制标题
通过图神经网络进行多机器人系统的覆盖控制
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Vijay R. Kumar
中科院分区：
文献类型：
--
作者：
W. Gosrich;Siddharth Mayya;Rebecca Li;James Paulos;Mark H. Yim;Alejandro Ribeiro;Vijay R. Kumar
本文提出了一种分散的方法，移动的传感器覆盖的多机器人系统。我们考虑的情况下，一个团队的机器人有限的传感范围必须定位自己，以有效地检测感兴趣的事件在一个区域的特点是不同的重要性。为此，我们开发了一个分散的控制策略的机器人实现通过图形神经网络，它使用机器人间的通信，利用非本地信息的控制决策。通过显式地共享多跳邻居之间的信息，分散控制器实现了更高的覆盖质量相比，经典的方法，不通信，只利用本地信息提供给每个机器人。仿真实验验证了多跳通信对多机器人覆盖的有效性，并评估了基于学习的控制器的可扩展性和可移植性。
This paper develops a decentralized approach to mobile sensor coverage by a multi-robot system. We consider a scenario where a team of robots with limited sensing range must position itself to effectively detect events of interest in a region characterized by areas of varying importance. Towards this end, we develop a decentralized control policy for the robots-realized via a Graph Neural Network-which uses inter-robot communication to leverage non-local information for control decisions. By explicitly sharing information between multi-hop neighbors, the decentralized controller achieves a higher quality of coverage when compared to classical approaches that do not communicate and leverage only local information available to each robot. Simulated experiments demonstrate the efficacy of multi-hop communication for multi-robot coverage and evaluate the scalability and transferability of the learning-based controllers.
影响因子：
20.6
作者：
Ruiz, Luana;Gama, Fernando;Ribeiro, Alejandro
通讯作者：
Ribeiro, Alejandro