Deep Direct Visual Servoing of Tendon-Driven Continuum Robots
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Vision-based control has found a key place in the research to tackle the requirement of the state feedback when controlling a continuum robot under physical sensing limitations. Traditional visual servoing requires feature extraction and tracking while the imaging device captures the images, which limits the controller's efficiency. We hypothesize that employing deep learning models and implementing direct visual servoing can effectively resolve the issue by eliminating the tracking requirement and controlling the continuum robot without requiring an exact system model. In this paper, we control a single-section tendon-driven continuum robot utilizing a modified VGG-16 deep learning network and an eye-in-hand direct visual servoing approach. The proposed algorithm is first developed in Blender using only one input image of the target and then implemented on a real robot. The convergence and accuracy of the results in normal, shadowed, and occluded scenes reflected by the sum of absolute difference between the normalized target and captured images prove the effectiveness and robustness of the proposed controller.
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