22 May 2015 Automatic behavior sensing for a bomb-detecting dog
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Abstract
Bomb-detecting dogs are trained to detect explosives through their sense of smell and often perform a specific behavior to indicate a possible bomb detection. This behavior is noticed by the dog handler, who confirms the probable explosives, determines the location, and forwards the information to an explosive ordnance disposal (EOD) team. To improve the speed and accuracy of this process and better integrate it with the EOD team’s robotic explosive disposal operation, SPAWAR Systems Center Pacific has designed and prototyped an electronic dog collar that automatically tracks the dog’s location and attitude, detects the indicative behavior, and records the data. To account for the differences between dogs, a 5-minute training routine can be executed before the mission to establish initial values for the k-mean clustering algorithm that classifies a specific dog’s behavior. The recorded data include GPS location of the suspected bomb, the path the dog took to approach this location, and a video clip covering the detection event. The dog handler reviews and confirms the data before it is packaged up and forwarded on to the EOD team. The EOD team uses the video clip to better identify the type of bomb and for awareness of the surrounding environment before they arrive at the scene. Before the robotic neutralization operation commences at the site, the location and path data (which are supplied in a format understandable by the next-generation EOD robots—the Advanced EOD Robotic System) can be loaded into the robotic controller to automatically guide the robot to the bomb site. This paper describes the project with emphasis on the dog-collar hardware, behavior-classification software, and feasibility testing.
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Hoa G. Nguyen, Hoa G. Nguyen, Adam Nans, Adam Nans, Kurt Talke, Kurt Talke, Paul Candela, Paul Candela, H. R. Everett, H. R. Everett, } "Automatic behavior sensing for a bomb-detecting dog", Proc. SPIE 9468, Unmanned Systems Technology XVII, 94680K (22 May 2015); doi: 10.1117/12.2177443; https://doi.org/10.1117/12.2177443
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