This paper describes the design of a simulation environment for a GPS / 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 this effort as smart sensor in order 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 Point Matching (PM) 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 and reference path generation have been implemented within the simulation environment. Mathematical model of the noise produced by GPS, MV, INS and pressure sensors are also derived. This paper also presents an Extended Kalman Filter (EKF) used for the sensors fusion between GPS and MV systems. Results on the accuracy reached for the estimation of the relative position are also provided.
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