Enhanced Adaptive Artificial Potential Field for UAV Navigation in Dynamic 3D Environments With Lightweight Spherical Obstacle Map

Effective navigation in dynamic three-dimensional (3D) environments is essential for autonomous unmanned aerial vehicles (UAVs), but existing methods often lack computational efficiency and robustness. To address these challenges, this paper presents a path planning framework that combines a lightweight mapping module and an adaptive planner. A spherical obstacle map is proposed to represent dynamic environments efficiently, converting dense point clouds into sparse spherical representations while estimating obstacle motion for safe planning. An enhanced adaptive artificial potential field based on the spherical obstacle map is introduced to improve path reachability and safety, integrating an attractive force with terminal acceleration to avoid terminal local minimum, a repulsive force with a 3D vortex to improve safety, and an emergency-deflection mechanism to mitigate intermediate local minimum. In addition, an adaptive path optimization algorithm dynamically tunes planning coefficients to prioritize reachability and safety, while also guaranteeing efficiency. Extensive simulations in dynamic sphere and pedestrian environments, along with a real-world UAV experiment, demonstrate that the proposed framework outperforms existing methods in success rate and safety, while maintaining balance in other performance metrics.