外观
论文arXiv 2608.168531 个月前
FlexWorm：面向基于吸附的多段可变形机器人的基元增强混合接触-运动规划
原文链接：FlexWorm: Primitive-Augmented Hybrid Contact-Motion Planning for Suction-Based Multi-Segment Deformable Robots（arXiv 2608.16853 · 发布日期：2026-09-02）
FlexWorm: Primitive-Augmented Hybrid Contact-Motion Planning for Suction-Based Multi-Segment Deformable Robots
针对多段吸附软机器人依赖手工步态的问题，提出结合IKHS与学习运动基元的混合规划，并在仿真和硬件实验验证。
核心要点
- 多段吸附软机器人适用于狭窄或脆弱环境，但现有方法依赖手工步态和环境特定脚本。
- 框架面向复杂曲面3D导航，处理离散吸附切换、连续身体变形及几何、碰撞、准静态约束。
- IKHS在可行吸附转换上最佳优先搜索，仅对诱导自由块求解逆运动学；PaHS检索短程验证运动段。
- 仿真中该框架在规划成功率、转换质量和效率上优于受控基线，PaHS大幅减少规划时间。
验证范围与局限：验证范围包括多样地形仿真，以及气动多段软机器人上的重复硬件实验。
英文摘要 (Abstract)
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.
作者：Zili Tang, Tiecheng Guo, Qinyue Zhang, Meng Guo
来源
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