Sampling-Based Follow-the-Leader Motion Planning for Manipulator-Mounted Continuum Robots

TL;DR

This paper introduces a sampling-based motion planner for continuum robots mounted on manipulators, enabling efficient, accurate follow-the-leader motion by decoupling shape search from base pose calculation.

Contribution

It presents a novel planning method that jointly considers robot shape and base pose, with offline computation and theoretical guarantees for manipulator-mounted continuum robots.

Findings

Achieved 0% tip error in simulated path planning.

Demonstrated 1.9% mean shape deviation relative to robot length.

Validated approach on a real 6-DOF tendon-driven continuum robot.

Abstract

Follow-the-leader (FTL) motion exploits the unique morphology of continuum robots (CRs) to navigate confined spaces by having the body retrace the path of the tip. While extensively studied, existing FTL methods typically assume a fixed base or a single degree-of-freedom insertion mechanism, limiting their applicability to practical systems in which CRs are mounted on robotic manipulators with fully actuated SE(3) base pose. This paper presents a sampling-based motion planner for FTL motion of manipulator-mounted CRs that jointly considers robot configuration and base pose. The key idea is to decouple global shape search from base pose determination by computing the base pose through a closed-form geometric construction, thereby avoiding iterative optimization during online planning. The approach supports general forward models and enables efficient planning by shifting the majority of computation offline. We establish theoretical guarantees including resolution complete shape search and converging tip tracking throughout waypoint traversal and interpolation. Experiments on 120 simulated paths over 3 test classes demonstrate 0% tip error and 1.9% mean shape deviation (w.r.t. robot length) at 100% success rate. We validate the practicality of our approach on a 6-DOF tendon-driven CR mounted on a serial manipulator. Code and visualization available at https://continuumroboticslab.github.io/sb-ftl-cr-planner/.