Enhanced Potential Field-Based Collision Avoidance for Unmanned Aerial Vehicles in a Dynamic Environment
Daegyun Choi, Kyuman Lee, Donghoon Kim
Abstract
Collision avoidance in aerial environments using the conventional artificial potential field (APF) often faces local minima problems and the results prevent unmanned aerial vehicles (UAVs) from performing their missions. In addition, an UAV’s paths planned based on the conventional method are safe trajectories only in a certain static environment. To generate optimal and collision-free paths in a dynamic environment, the authors propose a novel APF approach, called “enhanced curl-free vector field”. For the repulsive potential field of the approach proposed, one computes each angle between the velocity vectors of UAVs and the relative position vectors of moving obstacles to the UAVs. The comparisons of the computed angles and the velocity of UAVs determine the direction of the curl-free vector field. Results from two case simulations, static obstacles with local minima and dynamic obstacles, show that our approach solves the local minima problems of path planning and generates more efficient paths for avoiding potential collisions caused by dynamic obstacles compared to the existing APF methods.