Hybrid Model-Learning Decoupled Control for Tendon-Driven Multi-Segment Continuum Robotic Bronchoscope

Abstract

Flexible tendon-driven multi-segment robotic bronchoscopes can reach peripheral lung regions for minimally invasive diagnosis and therapy. However, long tendon transmissions introduce friction, elasticity, and backlash, which couple the motion of adjacent segments and reduce operational accuracy and safety. This paper proposes a hybrid model-learning decoupled control framework for a two-segment bronchoscope that explicitly cancels distal-to-proximal coupling while compensating transmission disturbances. The method learns online a pose-dependent coupling map from synchronized encoder and electromagnetic measurements and uses it for feedforward cancellation in the proximal channel. In addition, an adaptive disturbance compensation module estimates per-tendon compliance and backlash to correct stretch and dead-zone effects. A two-segment tendon-driven robotic bronchoscope platform demonstrated a substantial reduction in proximal drift during distal actuation. At a 90° distal bend, the mean proximal coupling angle was 5.84°. Compared with the most commonly used piecewise constant curvature model baseline, the proposed controller achieved stronger motion decoupling, reducing the coupling rate by 86.47%, thereby enabling more precise bronchoscopic manipulation in anatomically constrained environments.