Robotics & Machine Learning Daily News2024,Issue(Feb.5) :50-50.DOI:10.1109/TITS.2023.3333409

Study Results from Shandong University Update Understanding of Robotics (Multi-robot Environmental Coverage With a Two-stage Coordination Strategy Via Deep Reinforcement Learning)

Robotics & Machine Learning Daily News2024,Issue(Feb.5) :50-50.DOI:10.1109/TITS.2023.3333409

Study Results from Shandong University Update Understanding of Robotics (Multi-robot Environmental Coverage With a Two-stage Coordination Strategy Via Deep Reinforcement Learning)

扫码查看

Abstract

Research findings on Robotics are discussed in a new report. According to news reporting originating from Shandong, People's Republic of China, by NewsRx correspondents, research stated, “Multi-robot environmental coverage can be widely used in many applications like search and rescue. However, it is challenging to coordinate the robot team for high coverage efficiency.” Financial support for this research came from National Key Research and Development Project of New Generation Artificial Intelligence of China. Our news editors obtained a quote from the research from Shandong University, “In this paper, we propose a Two-Stage Coordination (TSC) strategy, which consists of a high-level leader module and a low- level action executor. The former provides the robots with the topology and geometry of the environment, which are crucial for robots to learn ‘where' they should go and avoid invalid coverage. Based on the observed information and the environmental topology, the latter module takes primitive action to reach the sub-goal. To facilitate cooperation among the robots, we aggregate local perception information of neighbors from different hops based on graph neural networks. We compare our method with state-of-the- art multi-robot coverage approaches.”

Key words

Shandong/People’s Republic of China/Asia/Emerging Technologies/Machine Learning/Nano-robot/Reinforcement Learning/Robot/Robotics/Shandong University

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
被引量1
参考文献量46
段落导航相关论文