Predictive Angular Potential Field-based Obstacle Avoidance for Dynamic UAV Flights

TL;DR

This paper introduces a real-time, LiDAR-based obstacle avoidance method for UAVs that enhances safety and speed by directly operating on range images without relying on global maps.

Contribution

The novel approach computes angular potential fields from LiDAR data for fast, reactive obstacle avoidance, maintaining UAV agility without high-level localization.

Findings

Maintains safe distances from obstacles in simulations

Allows higher flight velocities than previous methods

Operates in real time without global mapping

Abstract

In recent years, unmanned aerial vehicles (UAVs) are used for numerous inspection and video capture tasks. Manually controlling UAVs in the vicinity of obstacles is challenging, however, and poses a high risk of collisions. Even for autonomous flight, global navigation planning might be too slow to react to newly perceived obstacles. Disturbances such as wind might lead to deviations from the planned trajectories. In this work, we present a fast predictive obstacle avoidance method that does not depend on higher-level localization or mapping and maintains the dynamic flight capabilities of UAVs. It directly operates on LiDAR range images in real time and adjusts the current flight direction by computing angular potential fields within the range image. The velocity magnitude is subsequently determined based on a trajectory prediction and time-to-contact estimation. Our method is evaluated using Hardware-in-the-Loop simulations. It keeps the UAV at a safe distance to obstacles, while allowing higher flight velocities than previous reactive obstacle avoidance methods that directly operate on sensor data.