UAV Path Planning and Multi-Aircraft Collaboration Based on Machine Learning

Abstract

Unmanned Aerial Vehicle applications are expanding into dense urban airspace, and path planning needs to meet the needs of safe, efficient, and autonomous flight in complex dynamic environments. Traditional path planning methods have high computational complexity, poor real-time performance, and are difficult to deal with uncertainties such as noise and disturbance. Machine learning has become a core research direction. This article builds a three-layer framework of environmental modeling -single-machine decision-making, multi-machine collaboration, and systematically sorts out the key technologies of Unmanned Aerial Vehicle (UAV) path planning-- analytical three-dimensional grid, cylindrical coordinate system spatial modeling, and four-dimensional risk cost model; reviews improved A*, swarm intelligence, biologically inspired neural networks, and other algorithms, and compares performance differences through multi-dimensional matrices. Research shows that third-party risk modeling is the core of low-altitude safety planning, and the integration of biologically inspired neural networks and swarm intelligence can effectively solve complex obstacle avoidance problems. This article points out challenges such as multi-aircraft collaborative obstacle avoidance and distributed low-altitude intelligent connectivity, and looks forward to trends such as synaesthesia and computing integration, embodied intelligence integration, and green energy efficiency optimization, providing technical reference for the safety and engineering implementation of autonomous UAV flight.