FlexWorm：用于基于吸盘的多段可变形机器人的基元增强混合接触-运动规划
FlexWorm: Primitive-augmented Hybrid Contact-motion Planning for Suction-based Multi-segment Deformable Robots
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中文总结 AI 辅助
FlexWorm框架为基于吸盘的多段可变形机器人提出规划方法，含IKHS与PaHS，仿真和硬件实验表明其在复杂环境下规划性能更优且鲁棒性良好。
中文摘要 AI 辅助
基于吸盘的多段软机器人在受限或脆弱环境的巡检与维护中具有应用前景，但现有方法仍高度依赖人工设计的步态和针对特定环境的运动脚本。本文针对具有可变形体段和边界吸盘的串联式多段软机器人，提出一种规划框架，旨在实现复杂表面上的全三维导航，明确处理几何、碰撞和准静态可行性约束下的离散粘附切换与连续体变形，且不依赖实现体段变形的具体驱动方式。其核心是分块逆运动学混合搜索（IKHS），该算法在可行的粘附转换上执行最佳优先搜索，仅对诱导的自由块求解逆运动学。在IKHS基础上，基元增强混合搜索（PaHS）利用学习到的观测-基元嵌入来检索经过验证的短运动片段，以实现快速局部提议，当检索失败时则回退至标准IKHS分支。仿真实验表明，该框架在不同地形上的规划成功率、转换质量和效率上均显著优于受控基线；PaHS的成功率与IKHS相当，却大幅缩短了规划时间。对气动多段软机器人的重复硬件实验进一步验证了其在驱动和粘附不确定性下的可执行性与在线恢复能力。
英文摘要
Multi-segment suction-based soft robots are promising for inspection and maintenance in confined or fragile environments, but existing approaches still depend heavily on manually designed gaits and environment-specific motion scripts. This work presents a planning framework for serial multi-segment soft robots with deformable body segments and boundary suction pads. The formulation targets full 3D navigation on complex surfaces and explicitly handles discrete adhesion switching and continuous body deformation under geometric, collision, and quasi-static feasibility constraints, while remaining agnostic to the specific actuation realization used to produce segment deformation. Its core, block-wise IK hybrid search (IKHS), performs best-first search over feasible adhesion transitions while solving inverse kinematics only on induced free blocks. On top of IKHS, primitive-augmented hybrid search (PaHS) uses a learned observation--primitive embedding to retrieve short validated motion segments for fast local proposal, with fallback to standard IKHS branching when retrieval fails. In simulation, the framework consistently outperforms controlled baselines in planning success, transition quality, and efficiency across diverse terrains. PaHS matches IKHS in success rate while substantially reducing planning time. Repeated hardware experiments on a pneumatic multi-segment soft robot further demonstrate executability and online recovery under actuation and adhesion uncertainty.
发表机构
- School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）
机构由 AI 辅助整理，请以论文原文为准。
