A semi-autonomous robotic system with alignment control for the occluded object using visual servoing and computational intelligence

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ID: 285998
2024
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Abstract
Aligning a mobile X-ray system in challenging environments, such as bomb-surrounded areas, poses difficulties due to safety concerns and occlusion. This study introduces a novel approach employing 6D object pose estimation and pose correction by integrating DeepLabV3 and iterative dense fusion into the visual servoing mechanism. The robotic system exhibits enhanced accuracy in detecting and aligning the occluded known 3D object model x-ray source to film. Evaluation metrics, including intersection-over-union and mean average, demonstrate high accuracy percentages for detecting the body (98.91%), handle (96.57%), and aperture (89.54%). The mean IoU for each part of the 3D model portable X-ray source ranges from 65.69% to 76.42%. Pose estimation accuracy, assessed through the ADD metric, indicates superior performance for static pose estimation closer to the camera. Dynamic pose estimation exhibits higher average ADD metrics in scenes with total occlusion. The robustness metric reveals lower lost tracking counts in scenes without occlusion, emphasizing the algorithm's challenges in fully occluded scenarios. The film handler alignment system, characterized by kinematic formulation and actuator calibration data, shows minimal errors in X and Y actuation (
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persistent_1760657273_68f17f798f973 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rogelio, Jayson P.
Journal Malay Journal
Year 2024
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdd_ece/7
Keywords Keywords not found

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