AI 中文总结
本文提出一种基于能量变曲率模型和多点CBF-QP的统一驱动空间框架，实现多段腱驱动连续体机器人的实时全身安全运动生成，在动态场景中达到100%无碰撞成功率。
AI 中文摘要
腱驱动连续体机器人的实时运动生成需要对非均匀弯曲和全身碰撞避免进行精确建模。本文提出了一种用于平面多段腱驱动连续体机器人的统一驱动空间框架。基于能量的变曲率模型捕获了空间变化的腱间距和弯曲刚度，并为微分逆运动学和安全监控提供了解析雅可比矩阵。多点CBF-QP在障碍物运动和驱动速度约束下强制执行骨干间隙，而其决策维度仅取决于独立驱动段的数量。该模型与GVS参考值紧密吻合，最大曲率误差为$5.223 \times 10^{-2}\\,\mathrm{m}^{-1}$。在超过100次MuJoCo试验中，所提出的方法在静态和动态场景中分别实现了96%和100%的无碰撞成功率，而在没有CBF约束的情况下，成功率约为60%和80%。孔穿越测试进一步证明了在受限环境中的安全运动。使用600个骨干监控点，平均控制步长时间为6.66毫秒，展示了实时全身安全运动生成的能力。
英文摘要
Real-time motion generation for tendon-driven continuum robots requires accurate modeling of nonuniform bending and whole-body collision avoidance. This paper presents a unified actuation-space framework for planar multi-segment tendon-driven continuum robots. An energy-based variable-curvature model captures spatially varying tendon spacing and bending stiffness and provides analytical Jacobians for differential inverse kinematics and safety monitoring. A multipoint CBF-QP enforces backbone clearance under obstacle motion and actuation-velocity bounds, while its decision dimension depends only on the number of independently actuated segments. The model closely agrees with GVS references, with a maximum curvature error of $5.223 \times 10^{-2}\,\mathrm{m}^{-1}$. Over 100 MuJoCo trials, the proposed method achieves collision-free success rates of 96% and 100% in static and dynamic scenarios, respectively, compared with approximately 60% and 80% without CBF constraints. Hole-traversal tests further demonstrate safe motion in constrained environments. With 600 backbone monitoring points, the mean control-step time is 6.66 ms, demonstrating real-time whole-body safe motion generation.
