CR-Solver: 腱駆動連続体ロボット向けGPU高速化キネマティクスソルバー
CR-Solver: GPU-Accelerated Kinematics Solver for Tendon-driven Continuum Robots
腱駆動連続体ロボットの運動生成のための、GPU並列最適化に基づく2段階ソルバーを提案し、逆運動学・経路追従・軌道計画を統一的に扱い、高速かつ高精度な解を実現した。
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著者: Heqing Yang, Yang Yi, Linqing Zhong, Linjiang Huang, Si Liu
分類: cs.RO
原文アブストラクト
Continuum robots provide intrinsic compliance, high dexterity, and safe physical interaction, enabling navigation and manipulation in confined and unstructured environments. Despite recent advances in sensing and control, heightening the need for precise motion generation, most widely used planning libraries are grounded in rigid-body assumptions, creating a critical gap for fast and practical tools for continuum robots. To address this, we present CR-Solver, a two-stage, optimization-based solver for the motion generation of tendon-driven continuum robots. Our method unifies inverse kinematics, path following, and trajectory planning within a single constrained nonlinear optimization framework. Leveraging GPU-accelerated parallel optimization, CR-Solver delivers fast, accurate, and constraint-aware solutions. We validate our approach on three tasks, demonstrating significant speedups over traditional CPU-based solvers while achieving a consistently high success rate above 95% and millimeter-level accuracy. The solver is implemented in pure Python, reducing the barrier to adoption and offering a practical, extensible foundation for continuum robots' high-performance motion planning.
