A real-time computer vision algorithm for the identification and geolocation of ground targets was developed and implemented on the Penn State University / Applied Research Laboratory Unmanned Aerial Vehicle (PSU/ARL UAV) system. The geolocation data is filtered using a linear Kalman filter, which provides a smoothed estimate of target location and target velocity. The vision processing routine and estimator are coupled with an onboard path planning algorithm that optimizes the vehicle trajectory to maximize surveillance coverage of the targets.The vision processing and estimation routines were flight tested onboard a UAV system with a human pilot-in-the-loop. It was found that GPS latency had a significant effect on the geolocation error, and performance was significantly improved when using latency compensation. The combined target geolocation and path planning system was tested on the ground using a hardware-in-the-loop simulation, and resulted successful tracking and observation of a fixed target. Timing results showed that is is feasible to implement total system in real time.
Flight tests of a path planning algorithm using direct collocation with nonlinear programming (DCNLP) are presented. The path planner operates in real time onboard an unmanned aerial vehicle for these tests. The method plans a path that maximizes the time a target is in view of a camera onboard the aircraft. Tests include surveilling a stationary and moving target while compensating for any wind effects. Additionally, the effect of the use of road data in planning the path is simulated by tracking a second UAV flying a predefined pattern. Finally, a method of commanding the observation of a target from a specific line of sight is presented.
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