The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points ...The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV＇s autonomous landing.展开更多
Multiagent reinforcement learning(MARL)has become a dazzling new star in the field of reinforcement learning in recent years,demonstrating its immense potential across many application scenarios.The reward function di...Multiagent reinforcement learning(MARL)has become a dazzling new star in the field of reinforcement learning in recent years,demonstrating its immense potential across many application scenarios.The reward function directs agents to explore their environments and make optimal decisions within them by establishing evaluation criteria and feedback mechanisms.Concurrently,cooperative objectives at the macro level provide a trajectory for agents’learning,ensuring alignment between individual behavioral strategies and the overarching system goals.The interplay between reward structures and cooperative objectives not only bolsters the effectiveness of individual agents but also fosters interagent collaboration,offering both momentum and direction for the development of swarm intelligence and the harmonious operation of multiagent systems.This review delves deeply into the methods for designing reward structures and optimizing cooperative objectives in MARL,along with the most recent scientific advancements in this field.The article meticulously reviews the application of simulation environments in cooperative scenarios and discusses future trends and potential research directions in the field,providing a forward-looking perspective and inspiration for subsequent research efforts.展开更多
基金supported by the NUAA Fundamental Research Funds(No.NS2013034)
文摘The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV＇s autonomous landing.
基金supported by the Key Project of the National Language Commission(No.ZDI145-110)the Key Laboratory Project(No.YYZN-2024-6),the China Disabled Persons’Federation Project(No.2024CDPFAT-22)+3 种基金the National Natural Science Foundation of China(Nos.62171042,62102033,and U24A20331)the Project for the Construction and Support of High-Level Innovative Teams in Beijing Municipal Institutions(No.BPHR20220121)the Beijing Natural Science Foundation(Nos.4232026 and 4242020)the Projects of Beijing Union University(Nos.ZKZD202302 and ZK20202403)。
文摘Multiagent reinforcement learning(MARL)has become a dazzling new star in the field of reinforcement learning in recent years,demonstrating its immense potential across many application scenarios.The reward function directs agents to explore their environments and make optimal decisions within them by establishing evaluation criteria and feedback mechanisms.Concurrently,cooperative objectives at the macro level provide a trajectory for agents’learning,ensuring alignment between individual behavioral strategies and the overarching system goals.The interplay between reward structures and cooperative objectives not only bolsters the effectiveness of individual agents but also fosters interagent collaboration,offering both momentum and direction for the development of swarm intelligence and the harmonious operation of multiagent systems.This review delves deeply into the methods for designing reward structures and optimizing cooperative objectives in MARL,along with the most recent scientific advancements in this field.The article meticulously reviews the application of simulation environments in cooperative scenarios and discusses future trends and potential research directions in the field,providing a forward-looking perspective and inspiration for subsequent research efforts.
