Simulation Environment for Machine Vision Based Aerial Refueling for UAVs

Abstract

The design of a simulation environment is described for a machine vision (MV)-based approach for the problem of aerial refueling (AR) for unmanned aerial vehicles (UAVs) using the USAF refueling method. MV-based algorithms are implemented within the proposed scheme to detect the relative position and orientation between the UAV and the tanker. Within this effort, techniques and algorithms for the visualization the tanker aircraft in a virtual reality (VR) setting, for the acquisition of the tanker image, for the feature extraction (FE) from the acquired image, for the feature matching (FM) of the features, for the tanker-UAV pose estimation (PE) have been developed and extensively tested in closed-loop simulations. Detailed mathematical models of the tanker and UAV dynamics, refueling boom, turbulence, wind gusts, and tanker's wake effects, along with the UAV docking control laws have been implemented within the simulation environment. This paper also presents the results of a study relative to the use of passive markers versus feature extraction for the problem of estimating in real time the UAV-tanker relative position and orientation vectors.