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.
Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles
Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE), Nicosia, Cyprus（塞浦路斯尼科西亚交互式媒体、智能系统与新兴技术研究中心（RISE））；Department of Computer Science, University of Cyprus, Nicosia, Cyprus（塞浦路斯尼科西亚塞浦路斯大学计算机科学系）
Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review
Chef Robotics；RovifyLab；IT Application Research Center, Jeonbuk Regional Branch；Department of Electrical, Computer and Software Engineering（电气与计算机软件工程系）
Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
Prince Sultan University（苏丹王子大学）；University of Carthage（迦太基大学）；Gaitech Robotics（盖泰克机器人公司）；Polytechnic Institute of Porto（波尔图理工学院）；Al-Imam Mohamed bin Saud University（伊玛目穆罕默德·本·沙特伊斯兰大学）
