首页|Recent Findings in Robotics Described by Researchers from Beihang University (Multirobot Collaborative Task Dynamic Scheduling Based On Multiagent Reinforcement Learning With Heuristic Graph Convolution Considering Robot Service Performance)
Recent Findings in Robotics Described by Researchers from Beihang University (Multirobot Collaborative Task Dynamic Scheduling Based On Multiagent Reinforcement Learning With Heuristic Graph Convolution Considering Robot Service Performance)
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NETL
NSTL
Elsevier
A new study on Robotics is now available. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “To address the problem of multirobot collaborative task scheduling considering the degradation of industrial robot performance and the recovery of robot performance through intervention of compensation measures, a robot collaborative task scheduling method based on multiagent reinforcement learning with heuristic graph convolution is proposed in this paper. Five types of constraints between tasks and robots from the temporal and spatial dimensions are designed, and a graph structure with different connection forms is utilized to represent the tasks, robots, and their mutual constraints.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Defense Industrial Technology Development Program of China.
BeijingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotReinforcement LearningRobotRoboticsBeihang University
NETL
NSTL
Elsevier
