发表机构
Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对连续体机器人运动规划问题，提出CR-Solver求解器，它基于优化框架统一逆运动学等，利用GPU加速并行优化，在三项任务验证中比传统CPU求解器显著加速，成功率超95%精度达毫米级，且用纯Python实现，为高性能运动规划提供基础。
AI 中文摘要
连续体机器人具有内在柔顺性、高灵活性和安全的物理交互性，可在受限和非结构化环境中导航与操作。尽管传感和控制方面有进展，但多数规划库基于刚体假设，缺乏适用于连续体机器人的快速实用工具。为此提出CR-Solver，一种用于腱驱动连续体机器人运动生成的两阶段、基于优化的求解器。该方法在单个约束非线性优化框架中统一了逆运动学、路径跟踪和轨迹规划。利用GPU加速并行优化，能快速、准确且考虑约束地给出解决方案。在三项任务上验证，比传统CPU求解器显著加速，成功率超95%且精度达毫米级。该求解器用纯Python实现降低了采用门槛，为连续体机器人高性能运动规划提供实用且可扩展基础。
英文摘要
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.
CommentsIROS 2026
