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DLBVS system utilizes visual feedback from the second camera to align and connect a nozzle to an adapter precisely. It involves defining a desired pose estimation (ξrough*$$ {\upxi}_{\mathrm{rough}}^{\ast } $$ and ξfine*$$ {\upxi}_{\mathrm{fine}}^{\ast } $$) and continuously comparing it with the current pose estimation (ξrough(t)$$ {\upxi}_{\mathrm{rough}}\left(\mathrm{t}\right) $$ and ξfine(t)$$ {\upxi}_{\mathrm{fine}}\left(\mathrm{t}\right) $$) to calculate the error (erough(t)$$ {\mathrm{e}}_{\mathrm{rough}}\left(\mathrm{t}\right) $$ and efine(t)$$ {\mathrm{e}}_{\mathrm{fine}}\left(\mathrm{t}\right) $$).
Source publication
This study develops and evaluates a deep learning based visual servoing (DLBVS) control system for guiding industrial robots during aircraft refueling, aiming to enhance operational efficiency and precision. The system employs a monocular camera mounted on the robot's end effector to capture images of target objects—the refueling nozzle and bottom...
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