Design and development of an endoscopic robotic system and deep reinforcement learning path planning algorithm for fine-needle biopsy of liver lesions
Abstract This paper presents the design, development and evaluation of a novel robotic platform for endoscopic ultrasound-guided fine-needle biopsy of liver lesions. The system combines a four degrees-of-freedom (DoF) two-segment tendon-driven continuum robot (TDCR) endoscope with a two DoF superelastic nickel–titanium bevel-tip steerable needle. Needle path planning is achieved using NeedleNav, a soft actor–critic (SAC) deep reinforcement learning (DRL) model that generates collision-free trajectories to deep-seated lesions. This represents one of the first integrated systems combining a TDCR, steerable needle and DRL-based navigation, and the first application of a SAC to liver lesion targeting. Evaluation of the TDCR through tip tracking of circular, diamond-shaped and arc trajectories demonstrated a mean absolute error (MAE) of 13.19 mm. NeedleNav converged to obstacle avoidance trajectories in $$\sim $$ ∼ 2500 training episodes. Needle curvature was augmented by hand-fabricating notches on its distal section. Two needles with a 3-cm and 8-cm notched section were evaluated in a gelatine liver phantom, achieving an MAE of 21.78 mm and 14.86 mm, respectively, for obstacle avoidance trajectories. The system demonstrated observable path deflection compared to obstacle-free trajectories for the same targets. Together, these findings suggest the feasibility of our proposed solution, expanding the reach of endoscopic needle interventions to deep-seated lesions in the right lobe.