Resumen
With the rapid development of the big data era, Unmanned Aerial Vehicles (UAVs) are being increasingly adopted for various complex environments. This has imposed new requirements for UAV path planning. How to efficiently organize, manage, and express all kinds of data in complex scenes and intelligently carry out fast and efficient path planning for UAVs are new challenges brought about by UAV application requirements. However, traditional path-planning methods lack the ability to effectively integrate and organize multivariate data in dynamic and complicated airspace environments. To address these challenges, this paper leverages the theory of the three-dimensional subdivision of earth space and proposes a novel environment-modeling approach based on airspace grids. In this approach, we carried out the grid-based modeling and storage of the UAV flight airspace environment and built a stable and intelligent deep-reinforcement-learning grid model to solve the problem of the passage cost of UAV path planning in the real world. Finally, we designed multiple sets of experiments to verify the efficiency of the global subdivision coding system as an environmental organization framework for path planning compared to a longitude?latitude system and to demonstrate the superiority of the improved deep-reinforcement-learning model in specific scenarios.