With the advancement of the industrial Internet and the ongoing intelligent transformation of manufacturing,multi-robot cooperative operations in unmanned warehouse systems face critical challenges in communication ef...With the advancement of the industrial Internet and the ongoing intelligent transformation of manufacturing,multi-robot cooperative operations in unmanned warehouse systems face critical challenges in communication efficiency and real-time decision-making.Conventional path-planning algorithms are insufficient for cooperative scheduling in dynamic and complex environments,while existing multi-agent reinforcement learning(MARL)-based communication approaches often fail to determine appropriate communication targets or when to broadcast messages,resulting in excessive overhead and low efficiency.To address these limitations,this paper proposes a MARL-based communication optimization algorithm with graph representations.A graph-structured encoder is designed to intelligently select communication partners and optimize the communication topology.In addition,a graph information bottleneck mechanism is introduced to guide the graph neural network in learning minimally sufficient representations of communication messages.This mechanism maximizes the relevance of the representations to the cooperative task while minimizing dependence on the original communication graph,thereby enabling effective compression of redundant information.Experimental validation on a cooperative transportation task with warehouse robots in the robot operating system(ROS)and Gazebo simulation environment demonstrates that the proposed method reduces communication overhead by 79.0%and improves efficiency by a factor of 3.5,while maintaining a task success rate comparable to that of full-communication schemes.These results provide an efficient communication solution for large-scale multi-robot cooperative systems in industrial Internet scenarios.展开更多
In the robot soccer competition platform, the cur- rent confrontation decision-making system suffers from dif- ficulties in optimization and adaptability. Therefore, we pro- pose a new self-adaptive decision-making （...In the robot soccer competition platform, the cur- rent confrontation decision-making system suffers from dif- ficulties in optimization and adaptability. Therefore, we pro- pose a new self-adaptive decision-making （SADM） strategy. SADM compensates for the restrictions of robot physical movement control by updating the task assignment and role assignment module using situation assessment techniques. It designs a self-adaptive role assignment model that assists the soccer robot in adapting to competition situations similar to how humans adapt in real time. Moreover, it also builds an accurate motion model for the robot in order to improve the competition ability of individual robot soccer. Experimental results show that SADM can adapt quickly and positively to new competition situations and has excellent performance in actual competition.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62476225in part by the Major Research Project of National Natural Science Foundation of China under Grant 92267110+1 种基金in part by the National Key R&D Program of China under Grant 2023YFF0905604in part by the Fundamental Research Funds for the Central Universities under Grants D5000230340,D5000250351,and G25KY0612609.
文摘With the advancement of the industrial Internet and the ongoing intelligent transformation of manufacturing,multi-robot cooperative operations in unmanned warehouse systems face critical challenges in communication efficiency and real-time decision-making.Conventional path-planning algorithms are insufficient for cooperative scheduling in dynamic and complex environments,while existing multi-agent reinforcement learning(MARL)-based communication approaches often fail to determine appropriate communication targets or when to broadcast messages,resulting in excessive overhead and low efficiency.To address these limitations,this paper proposes a MARL-based communication optimization algorithm with graph representations.A graph-structured encoder is designed to intelligently select communication partners and optimize the communication topology.In addition,a graph information bottleneck mechanism is introduced to guide the graph neural network in learning minimally sufficient representations of communication messages.This mechanism maximizes the relevance of the representations to the cooperative task while minimizing dependence on the original communication graph,thereby enabling effective compression of redundant information.Experimental validation on a cooperative transportation task with warehouse robots in the robot operating system(ROS)and Gazebo simulation environment demonstrates that the proposed method reduces communication overhead by 79.0%and improves efficiency by a factor of 3.5,while maintaining a task success rate comparable to that of full-communication schemes.These results provide an efficient communication solution for large-scale multi-robot cooperative systems in industrial Internet scenarios.
文摘In the robot soccer competition platform, the cur- rent confrontation decision-making system suffers from dif- ficulties in optimization and adaptability. Therefore, we pro- pose a new self-adaptive decision-making （SADM） strategy. SADM compensates for the restrictions of robot physical movement control by updating the task assignment and role assignment module using situation assessment techniques. It designs a self-adaptive role assignment model that assists the soccer robot in adapting to competition situations similar to how humans adapt in real time. Moreover, it also builds an accurate motion model for the robot in order to improve the competition ability of individual robot soccer. Experimental results show that SADM can adapt quickly and positively to new competition situations and has excellent performance in actual competition.
