Soft Robot Optimal Control Via Reduced Order Finite Element Models

被引:19
作者
Tonkens, Sander [1 ]
Lorenzett, Joseph [2 ]
Pavone, Marco [2 ]
机构
[1] Stanford Univ, Dept Mech Engn, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Aeronaut & Astronaut, Stanford, CA 94305 USA
来源
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) | 2021年
关键词
SIMULATION; REDUCTION; DESIGN;
D O I
10.1109/ICRA48506.2021.9560999
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Finite element methods have been successfully used to develop physics-basixl models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing controllers for complex dynamic tasks such as trajectory optimization and trajectory tracking. However, finite element models are also typically very high-dimensional, which makes real-time control challenging. In this work we propose an approach for finite element model-based control of soft robots that leverages model order reduction techniques to significantly increase computational efficiency. In particular, a constrained optimal control problem is formulated based on a nonlinear reduced order finite element model and is solved via sequential convex programming. This approach is demonstrated through simulation of a cable-driven soft robot for a constrained trajectory tracking task, where a 9768-dimensional finite element model is used for controller design.
引用
收藏
页码:12010 / 12016
页数:7
相关论文
共 26 条
[1]  
[Anonymous], 2019, P IEEE C ROB AUT
[2]   An overview of approximation methods for large-scale dynamical systems [J].
Antoulas, AC .
ANNUAL REVIEWS IN CONTROL, 2005, 29 (02) :181-190
[3]  
Bern J. M., 2020, IEEE INT C SOFT ROB
[4]  
Bern JM, 2019, ROBOTICS: SCIENCE AND SYSTEMS XV
[5]  
Bruder D, 2019, ROBOTICS: SCIENCE AND SYSTEMS XV
[6]   Software toolkit for modeling, simulation, and control of soft robots [J].
Coevoet, E. ;
Morales-Bieze, T. ;
Largilliere, F. ;
Zhang, Z. ;
Thieffry, M. ;
Sanz-Lopez, M. ;
Carrez, B. ;
Marchal, D. ;
Goury, O. ;
Dequidt, J. ;
Duriez, C. .
ADVANCED ROBOTICS, 2017, 31 (22) :1208-1224
[7]   Model-based dynamic feedback control of a planar soft robot: trajectory tracking and interaction with the environment [J].
Della Santina, Cosimo ;
Katzschmann, Robert K. ;
Bicchi, Antonio ;
Rus, Daniela .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2020, 39 (04) :490-513
[8]  
Duriez C., 2013, P IEEE C ROB A1T
[9]   Structure-preserving, stability, and accuracy properties of the energy-conserving sampling and weighting method for the hyper reduction of nonlinear finite element dynamic models [J].
Farhat, Charbel ;
Chapman, Todd ;
Avery, Philip .
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2015, 102 (05) :1077-1110
[10]  
Faure F, 2012, SOFT ISSUE BIOMECHAN, V11, P283, DOI [10.1007/84152012125, DOI 10.1007/8415_2012_125]