Obstacle avoidance for platforms in three-dimensional environments

Abstract: The field of obstacle avoidance is a well-researched area. Despite this, research on obstacle avoidance in three dimensions is surprisingly sparse. For platforms which are able to navigate three-dimensional space, such as multirotor UAVs, such methods will become more common. In this thesis, an obstacle avoidance method, intended for a three-dimensional environment, is presented. First the method reduces the dimensionality of the three-dimensional world into two dimensions by projecting obstacle observations onto a two-dimensional spherical depth map, retaining information on direction and distance to obstacles. Next, the method accounts for the dimensions of the platform by applying a post-processing on the depth map. Finally, knowing the motion model, a look-ahead verification step is taken, using information from the depth map, to ensure that the platform does not collide with any obstacles by not allowing control inputs which leads to collisions. If there are multiple control input candidates after verification that lead to velocity vectors close to a desired velocity vector, a heuristic cost function is used to select one single control input, where the similarity in direction and magnitude of the resulting and desired velocity vector is valued. Evaluation of the method reveals that platforms are able to maintain distances to obstacles. However, more work is suggested in order to improve the reliability of the method and to perform a real world evaluation.