Paper ID: 2410.14305 • Published Oct 18, 2024
Optimizing Modeling of Continuum Robots: Integration of Lie Group Kinematics and Evolutionary Algorithms
Po-Yu Hsieh, June-Hao Hou
TL;DR
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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 properties
and complex kinematics present significant challenges in digital modeling and
effective control. This research proposes a novel computational framework that
integrates Lie group kinematics with an evolutionary algorithm (EA) to identify
optimal control coefficients for specific robot models. Our method starts by
generating datasets from physics-based simulations and fractional order
control, defining both ideal configurations and models to be optimized. By
using EA, we iteratively minimize deviations through two fitness objectives
\textemdash deviation mean squared error (\(\text{MSE}_1\)) and TCP vector
error (\(\text{MSE}_2\)) \textemdash to align the robot's backbone with the
desired configuration. Built on the Computer-Aided Design (CAD) platform
Grasshopper, this framework provides real-time visualization, enabling dynamic
control of robot configurations. Results show that the proposed method achieves
precise alignment of the robot's backbone with minimal computation. This
approach not only simplifies the coefficient identification process but also
demonstrates the advantages of EA in multi-objective optimization, contributing
to efficient modeling and control of continuum robots.