Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex g...Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups.However,previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups,which is a critical issue for modern multi-UAVs communication to address.To address this problem,we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game.We then propose a Hybrid Attention Multi-agent Reinforcement Learning(HAMRL)algorithm,which uses attention structures to learn the dynamic characteristics of the task environment,and it integrates hybrid attention mechanisms to establish efficient intra-and inter-group communication aggregation for information extraction and group collaboration.Experimental results show that in this comprehensive and challenging model,our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.展开更多
It is comment that unmanned aerial vehicles （UAVs） have limitation on information cap- turing in reality applications. Therefore, online method of motion planning is necessary for such UA- Vs. Gyroscopic force （GF...It is comment that unmanned aerial vehicles （UAVs） have limitation on information cap- turing in reality applications. Therefore, online method of motion planning is necessary for such UA- Vs. Gyroscopic force （GF） is used for obstacle avoidance as an online method. However, classical GF has shortcoming in generating orbit for UAV with high velocity because the GF results in a time- varying turning radius. Modified gyroscopic force （MGF） given by function of velocity can overcome this shortcoming and help get a more practical control law for avoidance. MGF can also be used to implement the guidance of UAV by designing particular active conditions. Interactions in forms of stress function and damping force are introduced so that an UAV group can have coordinated motion. By combining controls of MGF and interactions, motion planning of UAV group in obstacle environ- ment can be implemented.展开更多
基金supported by the Key Research and Development Program of Jiangsu Province of China(No.BE2022157)the National Natural Science Foundation of China(Nos.62303111,62076060,and 61932007)+1 种基金the Defense Industrial Technology Development Program(No.JCKY2021214B002)the Fellowship of China Postdoctoral Science Foundation(No.2022M720715).
文摘Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups.However,previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups,which is a critical issue for modern multi-UAVs communication to address.To address this problem,we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game.We then propose a Hybrid Attention Multi-agent Reinforcement Learning(HAMRL)algorithm,which uses attention structures to learn the dynamic characteristics of the task environment,and it integrates hybrid attention mechanisms to establish efficient intra-and inter-group communication aggregation for information extraction and group collaboration.Experimental results show that in this comprehensive and challenging model,our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.
基金Supported by the National Natural Science Foundation of China(61350010)
文摘It is comment that unmanned aerial vehicles （UAVs） have limitation on information cap- turing in reality applications. Therefore, online method of motion planning is necessary for such UA- Vs. Gyroscopic force （GF） is used for obstacle avoidance as an online method. However, classical GF has shortcoming in generating orbit for UAV with high velocity because the GF results in a time- varying turning radius. Modified gyroscopic force （MGF） given by function of velocity can overcome this shortcoming and help get a more practical control law for avoidance. MGF can also be used to implement the guidance of UAV by designing particular active conditions. Interactions in forms of stress function and damping force are introduced so that an UAV group can have coordinated motion. By combining controls of MGF and interactions, motion planning of UAV group in obstacle environ- ment can be implemented.
