  Path planning methods for the unmanned aerial vehicle (UAV) in goods delivery
have drawn great attention from industry and academics because of its
flexibility which is suitable for many situations in the "Last Kilometer"
between customer and delivery nodes. However, the complicated situation is
still a problem for traditional combinatorial optimization methods. Based on
the state-of-the-art Reinforcement Learning (RL), this paper proposed an
improved method to achieve path planning for UAVs in complex surroundings:
multiple no-fly zones. The improved approach leverages the attention mechanism
and includes the embedding mechanism as the encoder and three different widths
of beam search (i.e.,~1, 5, and 10) as the decoders. Policy gradients are
utilized to train the RL model for obtaining the optimal strategies during
inference. The results show the feasibility and efficiency of the model
applying in this kind of complicated situation. Comparing the model with the
results obtained by the optimization solver OR-tools, it improves the
reliability of the distribution system and has a guiding significance for the
broad application of UAVs.
