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This letter proposes a neural network (NN) aided remote unmanned aerialvehicle (UAV) online control algorithm, coined oHJB. By downloading a UAV'sstate, a base station (BS) trains an HJB NN that solves theHamilton-Jacobi-Bellman equation (HJB) in real time, yielding the optimalcontrol action. Initially, the BS uploads this control action to the UAV. Ifthe HJB NN is sufficiently trained and the UAV is far away, the BS uploads theHJB NN model, enabling to locally carry out control decisions even when theconnection is lost. Simulations corroborate the effectiveness of oHJB inreducing the UAV's travel time and energy by utilizing the trade-off betweenuploading delays and control robustness in poor channel conditions.

Remote UAV Online Path Planning via Neural Network Based Opportunistic Control