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

2026-03-19

AI Summary

This work addresses the critical need for real-time, stable, and formally safe whole-body obstacle avoidance in soft continuum robots operating in human-shared environments. Existing approaches either rely on computationally expensive online optimization or lack rigorous safety guarantees. To overcome these limitations, this study proposes the first closed-form CLF-CBF (Control Lyapunov Function–Control Barrier Function) controller tailored for soft continuum robots, analytically unifying stability and safety constraints without requiring online optimization. The method simultaneously ensures trajectory tracking stability and collision-free operation through a provably safe control law. Validated in both simulation and experiments on a tendon-driven soft robotic arm, the approach achieves precise three-dimensional obstacle avoidance and robust tracking performance, with computation speeds approximately 10× faster than quadratic programming and 100× faster than sampling-based planners, substantially enhancing real-world deployability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Large-scale security measurements

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.

Problem

• soft continuum robot
• collision avoidance
• real-time control
• safety guarantee
• whole-body avoidance

Innovation

• closed-form CLF-CBF
• soft continuum robot
• real-time collision avoidance
• analytical safety embedding
• tendon-driven manipulator