# Hybrid Visual Servoing of Tendon-driven Continuum Robots

arXiv: https://arxiv.org/abs/2502.14092 · HTML: https://mlsanity.com/paper/2502.14092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Visual Servoing from Deep Neural Networks](https://mlsanity.com/paper/1705.08940/) (2017)
- [Deep Direct Visual Servoing of Tendon-Driven Continuum Robots](https://mlsanity.com/paper/2111.02580/) (2021)

## Built on by (most cited first, 1 of 1)

- [Constraint-Aware Physics-Informed Neural Networks for Static Shape Estimation of Co-Manipulative Continuum Robots](https://mlsanity.com/paper/2608.26273/) (2026)

## Abstract (arXiv)

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.