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Parametric Optimization of Rigid Wheels for Planetary Surface Mobility ApplicationsDesign-Build-Test approaches for spaceflight hardware are time and cost intensive, which can result in suboptimal mechanism designs. Optimization-based approaches that utilize high-fidelity models and physics simulation could overcome these limitations while simultaneously speeding up the mechanical design process and reducing cost. In this work, we present a toolchain that enables the multi-objective optimization of rigid rover wheels for planetary surface mobility applications. The toolchain uses Chrono’s Continuous Representation Model (CRM) functionality to simulate granular soil and performs multi-objective parametric optimization on candidate rover wheels to meet a desired performance criterion. The resulting wheel design is then evaluated experimentally using a single-wheel testbed. We end with a discussion of how the toolchain can be extended to simultaneously co-optimize other system parameters, such as system power consumption and feedback control gains.
Document ID
20230017332
Acquisition Source
Glenn Research Center
Document Type
Presentation
Authors
A Schepelmann
(Glenn Research Center Cleveland, Ohio, United States)
K A Johnson
(Glenn Research Center Cleveland, Ohio, United States)
C M Creager
(Glenn Research Center Cleveland, Ohio, United States)
D W Munster
(Glenn Research Center Cleveland, Ohio, United States)
L Bakke
(University of Wisconsin–Madison Madison, Wisconsin, United States)
D Negrut
(University of Wisconsin–Madison Madison, Wisconsin, United States)
Date Acquired
November 28, 2023
Subject Category
Cybernetics, Artificial Intelligence and Robotics
Meeting Information
Meeting: Machine-Ground Interaction Consortium (MaGIC)
Location: Madison, WI
Country: US
Start Date: December 5, 2023
End Date: December 7, 2023
Sponsors: University of Wisconsin–Madison
Funding Number(s)
WBS: 596118.04.47.22
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
Single Expert
Keywords
Optimization
Mechanical Design
Machine Learning
Digital Twin
Artificial Intelligence
Automation
Robotics
High-Fidelity Simulation
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