用拓扑方法量化软体机器人的形态，提升医疗手术精度
Topology-Inspired Morphological Descriptor for Soft Continuum Robots
- 结合伪刚体模型与莫尔斯理论，统计方向投影的临界点
- 可离散表征多模态构型，实现形态分类与控制优化
- 适合研究软体机器人控制与医疗应用的学者参考
本文提出一种受拓扑启发的软体连续机器人形态描述符，通过将伪刚体（PRB）模型与莫尔斯理论相结合，实现对机器人形态的定量表征。该方法通过统计方向投影的临界点，能够对多模态构型进行离散表示，并支持形态分类。进一步地，将目标构型建模为优化问题，求解产生具有特定拓扑特征的平衡形状所需的驱动参数。所提出的框架为软体连续机器人的形态描述、分类与控制提供了统一方法，有望在微创手术和血管内介入等医疗应用中提升其精度与适应性。
原文摘要 · Abstract (English)
This paper presents a topology-inspired morphological descriptor for soft continuum robots by combining a pseudo-rigid-body (PRB) model with Morse theory to achieve a quantitative characterization of robot morphologies. By counting critical points of directional projections, the proposed descriptor enables a discrete representation of multimodal configurations and facilitates morphological classification. Furthermore, we apply the descriptor to morphology control by formulating the target configuration as an optimization problem to compute actuation parameters that generate equilibrium shapes with desired topological features. The proposed framework provides a unified methodology for quantitative morphology description, classification, and control of soft continuum robots, with the potential to enhance their precision and adaptability in medical applications such as minimally invasive surgery and endovascular interventions.
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