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Trajectory Optimization Algorithm of UAV Based on Pointer Network

UAVs solve the problem of insufficient computing resources because of their high mobility, low cost and high reliability. For the problem of limited UAV energy storage, an adaptive trajectory optimization scheme for multi-UAV-assisted unloading is designed, firstly, a UAV capability assessment method is proposed to evaluate the capability of different UAVs; then an adaptive clustering method is proposed to cluster the unloading area, which is divided according to the distance and the area density calculated by scanning the area, followed by the size of the area task and the UAV capability strengths and weaknesses are matched sequentially; finally, the problem is considered as a combinatorial optimization problem, and a pointer network is proposed to plan the trajectory of the UAV, and the parameters of the pointer network are trained with reinforcement learning to minimize the cost of the UAV in the mobile edge computing system and produce a near-optimal trajectory. Experimental results show that the proposed algorithm can significantly reduce the total system cost.