A Monocular Vision-Based Target Surveillance and Interception System Demonstrated in a Counter Unmanned Aerial System (CUAS) Application
Several events in the recent years involving hobby Unmanned Aerial Vehicles (UAVs), colloquially known as drones, have demonstrated a general lack in security regarding relatively slow moving, low flying aerial objects. Additionally, these events have demonstrated the ease of using hobby UAVs for illegal or illicit purposes. Although researchers and industry professionals are working simultaneously to find solutions to detecting hobby UAVs and countering any possible threats, there are no options available, at the time of this study, that provide a practical solution to the UAV problem. This dissertation presents a possible solution to the Counter Unmanned Aerial System (CUAS) problem. This dissertation presents an autonomous system, developed within the framework of the Robotic Operating System (ROS), that uses only a monocular camera on board a UAV to autonomously detect, track, and follow an unauthorized UAV for surveillance purposes. If the UAV is determined to be a threat, the UAV, under the control of the autonomous system, uses itself as a counter defense by targeting and intercepting the flight of the unauthorized UAV, causing an air-to-air collision that results in a kinetic kill of the unauthorized UAV. The surveillance mode of the autonomous system described within this dissertation was evaluated on the capability to follow the unauthorized UAV for a distance that is greater than 50% of the total course distance. The interception mode of the system was evaluated on the number of direct interceptions of the unauthorized UAV. Results of the experiments conducted to test both modes of the autonomous system showed that when the unauthorized UAV was detected, the surveillance mode of the autonomous system typically tracked and followed the target for an average 87.23% of the average total distance of the courses in the experiments. Additionally, the experiments resulted in a mere 6.7% of unintentionally interceptions during the surveillance experiments, which demonstrates the control of the system despite a lack of distance information available. Results for the interception mode showed that the system presented here had a direct interception rate of 40%. If a UAV were carrying a net, the rate of interception increases to 81.3%.
Matson, Purdue University.
Robotics|Artificial intelligence|Computer science
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