Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 6, Issue 3, Pages 4281-4288Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3067285
Keywords
Modeling; control; and learning for soft robots; visual servoing
Categories
Funding
- Natural Science Foundation of China [62073222, U1913204, U1813206]
- Shu Guang - Shanghai Municipal Education Commission
- Shanghai Education Development Foundation [19SG08]
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Soft continuum robot is favored in unstructured environments for its high flexibility and safe interaction ability. Accurate shape control is essential for improving its practicality. This study proposes a vision-based shape control scheme using curvature features and an image-based visual servoing controller for soft robot manipulators.
Soft continuum robot is of superiority in the applications in unstructured environments due to its high flexibility and safe interaction ability. Accurate shape control is regarded as one of the prerequisites to improve its practicability. Considering the situation where the 3D position signals are not available, this letter investigates the vision-based shape control scheme of a soft robot manipulator. The curvature features used to depict the reference image shape of the robot backbone curve are solved through nonlinear optimization with given constraints of two endpoints in pixel coordinates. Thereafter, an adaptive image-based visual servoing controller is designed to track the reference image shape features with an uncalibrated monocular camera. The proposed algorithm was tested on an eight-cable driving soft robot prototype and proved its validity in convergence to the desired image shape.
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