Deep Direct Visual Servoing of Tendon-Driven Continuum Robots
This work addresses the end-point positioning challenge for tendon-driven continuum robots, offering a more efficient control method, though it appears incremental as it builds on existing deep learning and visual servoing techniques.
The paper tackled the problem of inefficient visual servoing for continuum robots by proposing a deep learning-based direct visual servoing approach that eliminates intermediate feature extraction and pose estimation steps, achieving convergence and accuracy in normal, shadowed, and occluded scenes.
Vision-based control provides a significant potential for the end-point positioning of continuum robots under physical sensing limitations. Traditional visual servoing requires feature extraction and tracking followed by full or partial pose estimation, limiting the controller's efficiency. We hypothesize that employing deep learning models and implementing direct visual servoing can effectively resolve the issue by eliminating such intermediate steps, enabling control of a continuum robot without requiring an exact system model. This paper presents the control of a single-section tendon-driven continuum robot using a modified VGG-16 deep learning network and an eye-in-hand direct visual servoing approach. The proposed algorithm is first developed in Blender software 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 demonstrate the effectiveness and robustness of the proposed controller.