Continuum robots, known for their high flexibility and adaptability, offer immense potential for applications such as medical surgery, confined-space inspections, and wearable devices. However, their non-linear elastic nature and complex kinematics present significant challenges in digital modeling and visualization. Identifying the modal shape coefficients of specific robot configuration often requires plenty of physical experiments, which is time-consuming and robot-specific. To address this issue, this research proposes a computational framework that utilizes evolutionary algorithm (EA) to simplify the coefficient identification process. Our method starts by generating datasets using Lie group kinematics and physics-based simulations, defining both ideal configurations and models to be optimized. With the deployment of EA solver, the deviations were iteratively minimized through two fitness objectives \textemdash mean square error of shape deviation (\(\text{MSE}_1\)) and tool center point (TCP) vector deviation (\(\text{MSE}_2\)) \textemdash to align the robot's backbone curve with the desired configuration. Built on the Computer-Aided Design (CAD) platform Grasshopper, this framework provides real-time visualization suitable for development of continuum robots. Results show that this integrated method achieves precise alignment and effective identification. Overall, the objective of this research aims to reduce the modeling complexity of continuum robots, enabling precise, efficient virtual simulation before robot programming and implementation.

翻译：连续体机器人以其高柔性和适应性著称，在医疗手术、受限空间检测和可穿戴设备等领域展现出巨大潜力。然而，其非线性弹性特性和复杂运动学特性给数字化建模与可视化带来了重大挑战。识别特定机器人构型的模态形状系数通常需要进行大量物理实验，这一过程不仅耗时，且具有机器人特异性。为解决此问题，本研究提出一种利用进化算法简化系数识别过程的计算框架。该方法首先基于李群运动学和物理仿真生成数据集，定义理想构型与待优化模型。通过部署进化算法求解器，以两个适应度目标——形状偏差均方误差（\(\text{MSE}_1\)）与工具中心点向量偏差（\(\text{MSE}_2\)）——迭代最小化偏差，使机器人骨干曲线与目标构型对齐。该框架构建于计算机辅助设计平台Grasshopper之上，提供了适用于连续体机器人开发的实时可视化功能。结果表明，该集成方法实现了精确对齐与有效识别。总体而言，本研究旨在降低连续体机器人的建模复杂度，为机器人编程与实施前提供精确高效的虚拟仿真。