全身連続体ソフトロボットの衝突回避のための閉形式CLF-CBFコントローラ
A Closed-Form CLF-CBF Controller for Whole-Body Continuum Soft Robot Collision Avoidance
オンライン最適化を必要とせず、安全性制約を解析的に制御入力に組み込んだ閉形式のCLF-CBFコントローラを提案し、ソフト連続体マニピュレータのリアルタイム3D障害物回避を実現した。
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著者: Kiwan Wong, Maximillian Stölzle, Wei Xiao, Daniela Rus
分類: cs.RO
原文アブストラクト
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.
