Evaluation of Machine Vision Algorithms for Autonomous Aerial Refueling for Unmanned Aerial Vehicles

The use of a combined Machine Vision (MV) and GPS-based approach has been recently proposed in simulation efforts as an alternative approach to ‘pure GPS’ for the problem of Autonomous Aerial Refueling (AAR) for Unmanned Aerial Vehicles (UAVs). While MV has appealing capabilities, a few critical issues need to be addressed for the actual implementation of MV for the AAR problem. For this purpose a simulation environment was developed featuring an interaction with a 3D Virtual Reality (VR) interface that generates an image stream of the AAR maneuver. The image flow is processed by the MV algorithm, providing, as output, a vector of the estimates of the relative tanker-UAV distance and attitude. This signal is then used by the UAV feedback control laws for ‘tracking & docking’ to the refueling boom. The MV algorithm specifically provides image processing for the isolation of optical markers, which are located at specific points on the tanker, extraction of the marker center of gravity, marker matching algorithm, and pose estimation algorithm for the final evaluation of the relative distance vector. Within this effort emphasis was placed on the development of an ‘ad-hoc’ feature matching algorithm followed by a comparative analysis of the performance of different matching algorithms. The paper presents a detailed analysis of the results from open loop and closed loop simulation of the different MV algorithms.