Path Planning with Potential Field-Based Obstacle Avoidance in a 3D Environment by an Unmanned Aerial Vehicle

In this paper we address the problem of path planning in an unknown environment with an aerial robot. The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 3D sensors, such as LiDARs. It performs obstacle avoidance in real time and on an on-board computer. We present a novel algorithm based on the conventional Artificial Potential Field (APF) that corrects the planned trajectory to avoid obstacles. To this end, our modified algorithm uses a rotation-based component to avoid local minima. The smooth trajectory following, achieved with the MPC tracker, allows us to quickly change and re-plan the UAV trajectory. Comparative experiments in simulation have shown that our approach solves local minima problems in trajectory planning and generates more efficient paths to avoid potential collisions with static obstacles compared to the original APF method.

Automated summary

This paper addresses the problem of path planning for an aerial robot in unknown environments. The proposed algorithm performs real-time obstacle avoidance using 3D sensors, and utilizes a rotation-based component to avoid local minima. Smooth trajectory following with an MPC tracker enables quick changes and re-planning of the UAV trajectory.