Selective Velocity Obstacle method for Autonomous Collision Avoidance: An experimental validation
Cherry Cheung · Research Repository (Delft University of Technology) · 2016
Unmanned aerial vehicle (UAV) applications are increasing and there is a need for safe operations in terms of avoidance. The Velocity Obstacle (VO) method uses position and velocity vectors to determine if a collision is going to happen; an adaptation of the VO- method is called the Selective Velocity Obstacle (SVO) method and adds navigation modes and right of way rules for cooperative flights. The characteristics of the SVO-method have been evaluated before in a simulated environment, but the contribution of this paper is an experimental validation of the SVO-method by including the factors that are neglected in simulation such as noise, delay and unmodeled dynamics. Additionally, it is shown how adaptations need to be made when actual drones are used for avoidance. Multiple situations are tested where two UAVs are flown on trajectories to create colliding situations. For the experimental setup, the SVO-method is implemented on a Parrot® AR.Drone 2.0 while an OptiTrack system provides position and velocity data. The Paparazzi autopilot system uses this data for its flight plans to fly autonomously between waypoints. The results of the experiment show that the SVO-method is a safe cooperative avoidance method.