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Operations involving autonomous vehicles require knowledge of the surrounding environment including other moving vehicles. The use of vision has been regarded as an enabling technology that can provide such information. Several important applications that would benefit from this technology is autonomous aerial refueling (AAR) and target tracking. This paper considers a sensor fusion approach using traditional IMU/GPS sensors with vision to facilitate the state estimation problem of moving targets. The proposed method makes use of a moving monocular camera to estimate the relative position and orientation of targets within the image by exploiting a known reference motion. The vision state estimation problem is solved using an homography approach that employs images containing both the reference and target vehicles. A simulation involving an unmanned aerial vehicle (UAV) and two ground vehicles is documented in this paper to demonstrate the algorithm and its accuracy.

Unmanned aerial vehicles (UAVs) stand to play a significant role in future sensing and information gathering missions. The scope of these mission scenarios is expanding to include those missions for which the sensor and carrier vehicle will be in close proximity to the surrounding environment, such as in urban operations. Several unique problems related to guidance, navigation and control are introduced that separate these tasks from the existing paradigm for information gathering missions at standoff range. This paper examines the challenges related to autonomous sensor planning missions in these close proximity environments and discusses solution strategies to achieve maximal sensing effectiveness. Specifically, results from vision-based navigation research are discussed and the concept of a geometric sensing effectiveness criterion is introduced and subsequently utilized for motion planning.

Missions envisioned for micro air vehicles may require a high degree of autonomy to operate in unknown environments. As such, vision is a critical technology for mission capability. This paper discusses an autopilot that uses vision coupled with GPS and altitude sensors. One use of vision processing analyzes a horizon to estimate roll and pitch information. Another use tracks a feature point to estimate position relative to a target. This paper presents examples of waypoint navigation and homing using vision-based feedback. The examples indicate the vision provides sufficient information to achieve the missions.

This paper documents the development of the capability to test MAVs (Micro Air Vehicles) in the University of Florida’s wind tunnel facility. The main goal of this work was to obtain, with a reliable procedure, good quality experimental data from wind tunnel tests of air vehicles at low Reynolds numbers, in the order of 100,000. An overview of the instrumentation and data analysis techniques will be presented, followed by some samples of results from tests on specific aircraft. A standard aerodynamic characterization test was developed to perform a “quick” System Identification (SID) characterization of an air vehicle. The requirements for those tests were established by the modeling and control portion of the project. The test procedure was aimed to find the main aerodynamic derivatives that will be used to model the aircraft and design the flight control system. Three distinctly different vehicles ranging in size from 60 cm to 15 cm wingspan are discussed.