用进化算法优化柔性机器人建模，实现快速精准虚拟仿真。
Visualization and Optimization of Continuum Robots: Integration of Lie Group Kinematics and Evolutionary Algorithm
- 结合李群运动学与物理仿真生成数据集，驱动进化算法求解形态参数。
- 在 Grasshopper 平台实现实时可视化，使机器人路径误差降低至 0.5% 以下。
- 适合需要快速原型设计的医疗机器人与复杂空间作业场景开发者。
柔性机器人因其高柔性和适应性，在医疗手术、狭小空间检测和可穿戴设备等领域具有巨大潜力。然而，其非线性弹性特性和复杂运动学给数字建模与可视化带来挑战。传统确定特定构型的模态系数需大量物理实验，耗时且依赖具体机器人。为此，本研究提出一种基于进化算法（EA）的计算框架，通过李群运动学与物理仿真生成数据集，构建理想构型与待优化模型。利用双目标适应度函数（形状偏差均方误差 ext{MSE}_1 与工具中心点向量偏差 ext{MSE}_2）迭代优化，使机器人主干曲线逼近目标构型。该框架基于计算机辅助设计平台 Grasshopper 实现，支持实时可视化，显著降低建模复杂度，提升虚拟仿真精度与效率，为机器人编程与实施前提供可靠预演。
原文摘要 · Abstract (English)
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.
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