A Closed-Form CLF-CBF Controller for Whole-Body Continuum Soft Robot Collision Avoidance

TL;DR

This paper introduces a fast, closed-form CLF-CBF controller for soft continuum robots that enables real-time collision avoidance with formal safety guarantees, outperforming traditional optimization and planning methods.

Contribution

It presents a novel analytical control approach embedding safety constraints directly, eliminating the need for online optimization in soft robot collision avoidance.

Findings

Up to 10x faster than quadratic programming approaches

Up to 100x faster than sampling-based planners

Demonstrated accurate trajectory tracking and obstacle avoidance in experiments

Abstract

Safe operation is essential for deploying robots in human-centered 3D environments. Soft continuum manipulators provide passive safety through mechanical compliance, but still require active control to achieve reliable collision avoidance. Existing approaches, such as sampling-based planning, are often computationally expensive and lack formal safety guarantees, which limits their use for real-time whole-body avoidance. This paper presents a closed-form Control Lyapunov Function--Control Barrier Function (CLF--CBF) controller for real-time 3D obstacle avoidance in soft continuum manipulators without online optimization. By analytically embedding safety constraints into the control input, the proposed method ensures stability and safety under the stated modeling assumptions, while avoiding feasibility issues commonly encountered in online optimization-based methods. The resulting controller is up to $10\times$ faster than standard CLF--CBF quadratic-programming approaches and up to $100\times$ faster than traditional sampling-based planners. Simulation and hardware experiments on a tendon-driven soft manipulator demonstrate accurate 3D trajectory tracking and robust obstacle avoidance in cluttered environments. These results show that the proposed framework provides a scalable and provably safe control strategy for soft robots operating in dynamic, safety-critical settings.