Learning-Based Control Strategies for Soft Robots

被引：39

作者：

Laschi, Cecilia ^{[1
]}

Thuruthel, Thomas George ^{[2
,3
]}

Lida, Fumiya ^{[3
,4
]}

Merzouki, Rochdi ^{[3
,5
]}

Falotico, Egidio ^{[3
,6
]}

机构：

[1] Natl Univ Singapore, Singapore 117575, Singapore

[2] UCL, Dept Comp Sci, London E20 2AF, England

[3] IEEE, Piscataway, NJ USA

[4] Univ Cambridge, Dept Engn, Robot, Cambridge CB2 1PZ, England

[5] Univ Lille, Polytech Lille, Robot, F-59655 Villeneuve Dascq, France

[6] Scuola Super Sant Anna, BioRobot Inst, I-56127 Pisa, Italy

来源：

IEEE CONTROL SYSTEMS MAGAZINE | 2023年 / 43卷 / 03期

基金：

新加坡国家研究基金会;

关键词：

POSITION CONTROL; DYNAMIC CONTROL; CONTROLLABILITY; MANIPULATORS; KINEMATICS; TRACKING; MODELS;

D O I：

10.1109/MCS.2023.3253421

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the last few decades, soft robotics technologies have challenged conventional approaches by introducing new, compliant bodies to the world of rigid robots. These technologies and systems may enable a wide range of applications, including human-robot interaction and dealing with complex environments. Soft bodies can adapt their shape to contact surfaces, distribute stress over a larger area, and increase the contact surface area, thus reducing impact forces. © 1991-2012 IEEE.

引用

页码：100 / 113

页数：14

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