Depth-based Visual Predictive Control of Tendon-Driven Continuum Robots

摘要

Image-based visual servoing (IBVS) scheme has become popular due to its advantages such as robustness to camera calibration errors and enabling direct control over the image features. However, in IBVS approaches several issues such as image singularities, and camera retreat problems must be addressed. Also, they lack a mechanism for enforcing constraints into control formulation. This paper presents a novel depth-based visual predictive control (DVPC) method to overcome several shortcomings of the previous IBVS methods. In particular, the proposed approach enables constraints enforcement and addresses one of the critical issues of the IBVS method, namely camera retreat problem. Furthermore, the proposed approach is applied for the control of continuum robots (CR). Simulation results are presented to verify the efficiency and functionality of the proposed approach.