为机械臂搭载的连续体机器人设计高效路径规划方法，实现精准跟踪与高成功率。
Sampling-Based Follow-the-Leader Motion Planning for Manipulator-Mounted Continuum Robots
- 通过几何解析法解耦全局形状与基座姿态，避免在线迭代优化。
- 120条路径测试中成功率达100%，末端误差为0，平均形变仅1.9%。
- 适用于高自由度机械臂上的连续体机器人，适合工业操作场景。
跟随领导者（FTL）运动利用连续体机器人（CRs）的独特形态，在狭窄空间中通过主体重走尖端路径实现导航。现有方法通常假设基座固定或仅单自由度插入，限制了在机械臂搭载的全自由度SE(3)基座系统中的应用。本文提出一种基于采样的FTL运动规划方法，联合考虑机器人构型与基座姿态。核心思想是通过闭式几何构造计算基座姿态，将全局形状搜索与基座确定解耦，从而在在线规划中避免迭代优化。该方法支持通用前向模型，多数计算可离线完成，提升效率。理论证明具有分辨率完备的形状搜索能力，并保证路径点遍历与插值过程中的收敛尖端追踪。在3类共120条模拟路径上验证，成功率达100%，末端误差为0，平均形状偏差为1.9%（相对于机器人长度）。在6自由度绳驱动连续体机器人搭载于串联机械臂的实验中验证了实际可行性。代码与可视化见https://continuumroboticslab.github.io/sb-ftl-cr-planner/。
原文摘要 · Abstract (English)
Follow-the-leader (FTL) motion exploits the unique morphology of continuum robots (CRs) to navigate confined spaces by having the body retrace the path of the tip. While extensively studied, existing FTL methods typically assume a fixed base or a single degree-of-freedom insertion mechanism, limiting their applicability to practical systems in which CRs are mounted on robotic manipulators with fully actuated SE(3) base pose. This paper presents a sampling-based motion planner for FTL motion of manipulator-mounted CRs that jointly considers robot configuration and base pose. The key idea is to decouple global shape search from base pose determination by computing the base pose through a closed-form geometric construction, thereby avoiding iterative optimization during online planning. The approach supports general forward models and enables efficient planning by shifting the majority of computation offline. We establish theoretical guarantees including resolution complete shape search and converging tip tracking throughout waypoint traversal and interpolation. Experiments on 120 simulated paths over 3 test classes demonstrate 0% tip error and 1.9% mean shape deviation (w.r.t. robot length) at 100% success rate. We validate the practicality of our approach on a 6-DOF tendon-driven CR mounted on a serial manipulator. Code and visualization available at https://continuumroboticslab.github.io/sb-ftl-cr-planner/.
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