首页|Study Results from Shandong University Update Understanding of Robotics (Multi-robot Environmental Coverage With a Two-stage Coordination Strategy Via Deep Reinforcement Learning)
Study Results from Shandong University Update Understanding of Robotics (Multi-robot Environmental Coverage With a Two-stage Coordination Strategy Via Deep Reinforcement Learning)
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NETL
NSTL
IEEE
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.”
ShandongPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotReinforcement LearningRobotRoboticsShandong University
NETL
NSTL
IEEE
