为绳驱柔性机器人设计了高速高精度的运动规划求解器。
CR-Solver: GPU-Accelerated Kinematics Solver for Tendon-driven Continuum Robots
- 分两阶段优化，统一处理逆运动学与轨迹规划。
- 在多种任务中实现95%以上成功率和毫米级精度。
- 基于GPU加速，纯Python实现，易用可扩展。
连续体机器人具有内在柔顺性、高灵活性和安全物理交互能力，适用于狭窄和非结构化环境中的导航与操作。尽管传感与控制技术不断进步，对精确运动生成的需求日益迫切，但当前主流规划库仍基于刚体假设，难以满足柔性机器人快速实用的运动规划需求。为此，我们提出CR-Solver，一种针对绳驱连续体机器人的两阶段、基于优化的运动生成求解器。该方法将逆运动学、路径跟踪与轨迹规划统一于单一约束非线性优化框架中。利用GPU加速的并行优化，CR-Solver实现快速、准确且约束感知的解算。我们在三个任务上验证该方法，结果表明相比传统CPU求解器有显著提速，成功率稳定超过95%，定位精度达毫米级。求解器采用纯Python实现，降低使用门槛，为连续体机器人的高性能运动规划提供实用且可扩展的基础。
原文摘要 · Abstract (English)
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.
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