Hybrid Visual Servoing of Tendon-driven Continuum Robots
Journal:
arXiv
Published Date:
Feb 19, 2025
Abstract
This paper introduces a novel Hybrid Visual Servoing (HVS) approach for
controlling tendon-driven continuum robots (TDCRs). The HVS system combines
Image-Based Visual Servoing (IBVS) with Deep Learning-Based Visual Servoing
(DLBVS) to overcome the limitations of each method and improve overall
performance. IBVS offers higher accuracy and faster convergence in feature-rich
environments, while DLBVS enhances robustness against disturbances and offers a
larger workspace. By enabling smooth transitions between IBVS and DLBVS, the
proposed HVS ensures effective control in dynamic, unstructured environments.
The effectiveness of this approach is validated through simulations and
real-world experiments, demonstrating that HVS achieves reduced iteration time,
faster convergence, lower final error, and smoother performance compared to
DLBVS alone, while maintaining DLBVS's robustness in challenging conditions
such as occlusions, lighting changes, actuator noise, and physical impacts.
