Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs

In this paper we address the problem of trajectory following in an unknown environment with an unmanned aerial vehicle (UAV). The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 2D or 3D sensors, such as LiDARs. We present a novel algorithm based on the conventional Artificial Potential Field (APF) called Augmented Artificial Potential Field (AAPF) that corrects the planned path to avoid obstacles. Our proposed algorithm uses a combination of two attractive forces and both normal and rotational repulsive forces to avoid obstacles and handle local minima problems. The smooth trajectory following achieved with the MPC tracker allows us to quickly change and re-plan the UAV path. Comparative simulation experiments have shown that our approach solves local minima problems in trajectory following and generates more efficient paths to avoid potential collisions with static obstacles compared to our previously developed algorithm for obstacle avoidance. The laboratory experimental evaluation results indicate that the algorithm can be deployed on a real UAV with limited computational power and real-time processing requirements.