  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 Artifcial Potential Field (APF) that
corrects the planned trajectory to avoid obstacles. To this end, our modifed
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 effcient paths to avoid potential collisions with
static obstacles compared to the original APF method.

Robotics