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

页码：12010 / 12016

页数：7

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