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In recent years, unmanned aerial vehicles (UAVs) are used for numerousinspection and video capture tasks. Manually controlling UAVs in the vicinityof obstacles is challenging, however, and poses a high risk of collisions. Evenfor autonomous flight, global navigation planning might be too slow to react tonewly perceived obstacles. Disturbances such as wind might lead to deviationsfrom the planned trajectories.  In this work, we present a fast predictive obstacle avoidance method thatdoes not depend on higher-level localization or mapping and maintains thedynamic flight capabilities of UAVs. It directly operates on LiDAR range imagesin real time and adjusts the current flight direction by computing angularpotential fields within the range image. The velocity magnitude is subsequentlydetermined based on a trajectory prediction and time-to-contact estimation.  Our method is evaluated using Hardware-in-the-Loop simulations. It keeps theUAV at a safe distance to obstacles, while allowing higher flight velocitiesthan previous reactive obstacle avoidance methods that directly operate onsensor data.

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