Research on low altitude logistics path planning and obstacle avoidance strategy for unmanned aerial vehicles based on DRL

With the rapid development of drone technology, low altitude logistics has become an important means to improve transportation efficiency and reduce costs. However, low altitude logistics drones face complex environmental and constraint conditions in path planning and obstacle avoidance, especially in achieving efficient and safe flight in narrow, which has become a key issue. Therefore, this study aims to explore low altitude logistics path planning and obstacle avoidance strategies for unmanned aerial vehicles based on DRL, and propose an optimized path planning method to improve the efficiency and safety of path planning. This study first analyzed the flight model of logistics drones and constructed dynamic and local path planning models for logistics drone path planning based on the characteristics of low altitude environments. The research method adopts the DDPG algorithm in DRL, and further optimizes the path planning process through an improved IDDPG algorithm. The core of the research lies in learning how drones can avoid obstacles in complex low altitude environments and optimize flight paths, reduce power consumption, and flight time while meeting safety constraints. Through simulation experiments, this article compares the performance of traditional artificial potential field method and DRL algorithms in path planning. The experimental results show that the IDDPG algorithm exhibits significant optimization effects in terms of path points, flight range, and power consumption. Especially in terms of path points, the IDDPG algorithm reduces 42.9% compared to traditional algorithms, while optimizing flight time and power consumption, demonstrating stronger adaptability and robustness. The innovation of this study lies in proposing a low altitude logistics path planning and obstacle avoidance strategy for unmanned aerial vehicles based on DRL, which combines the dynamic characteristics of unmanned aerial vehicles with low altitude airspace constraints to optimize the performance of traditional path planning algorithms.