UAV path intelligent planning in IoT data collection - Shu FU; Xiangyue YANG; Haijun ZHANG; Chen CHEN; Peng YU; Xin JIAN; Min LIU (Tongxin xuebao)
To solve the problem of path planning of UAV data collection, it was generally be divided into global path planning and local path planning.For global path planning, it was modeled as an orientation problem, which was a combination of two classical optimization problems, the knapsack problem and the traveling salesman problem.The pointer network of deep learning was used to solve the model to obtain the service node set and service order under the energy constraint of the UAV.In terms of the local path planning, the reference signal strength (RSS) of the sensor node received by UAV was employed to learn the local flight path of UAV by deep Q network, which enabled the UAV to approach and serve the nodes.Simulation results show that the proposed scheme can effectively improve the revenue of UAV data collection under the energy constraint of UAV.