基于可见光通信的多无人机协同数据采集及路径规划OA

With the advancement of communication technologies,traditional Radio Frequency(RF)communication confronts numerous challenges in electromagnetically sensitive or heavily interfered environments.Visible Light Communication(VLC)possesses advantages such as anti-interference capability,abundant spectrum resources,and high transmission rate,enabling it to ensure the reliability of data transmission in electromagnetically sensitive environments.Equipping Unmanned Aerial Vehicle(UAV)with VLC base stations allows full utilization of the high mobility of UAVs,effectively overcoming the coverage limitations of fixed VLC base stations.Consequently,UAV-aided VLC systems exhibit great potential in data acquisition tasks under complex environments,and are particularly suitable for the efficient collection and transmission of massive node information in the Internet of Things(IoT).VLC communication system with multi-UAV collaboration is studied,and by comprehensively considering the impact of flight jitter on communication channels,a task allocation method based on an improved K-means clustering algorithm is designed.Furthermore,a 3D trajectory planning method for UAVs based on the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is explored.The performance of the proposed methods is verified through simulation experiments,aiming to provide theoretical support and technical references for intelligent communication systems.Simulation results indicate that when the clustering factor w is 0.3,the proposed improved K-means clustering+TD3 path planning algorithm achieves superior system performance compared to baseline algorithms.Specifically,in comparison with baseline algorithms like SCAN,the proposed algorithm can effectively reduce the total flight distance of all UAVs by approximately 56%.