Small UAV Trajcetory Prediction and Avoidance using Monocular Computer Vision

Abstract

Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and the POSIT algorithm uses these feature points to estimate the pose (position and attitude) of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm's performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance.