期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 3081-3088出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3061311
关键词
Robots; Soft robotics; Finite element analysis; Kinematics; Manipulator dynamics; Iron; End effectors; Gain-scheduling; modeling; robust control; soft robot; uncertainties
类别
资金
- National Natural Science Foundation of China [62073081]
- Project of Department of Education of Guangdong Province [2019KZDXM037]
- Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology [2020B1212030010]
- project ROBOCOP [ANR-19-CE19]
- project COSSEROOTS [ANR-20-CE33]
- project Inventor (I-SITE ULNE, le programme d'Investissements d'Avenir, Metropole Europeenne de Lille, France)
Soft robotics has unique characteristics such as compliance and flexibility, providing promising features for applications such as safe human machine interaction and adaptability to unknown environments. A gain-scheduling closed-loop method is demonstrated to control a soft trunk robot within its workspace, considering its dynamic characteristics. The method is validated through experimental testing with convincing results.
Soft robotics has recently become an emergent research area due to its unique characteristics compared to conventional rigid robots. Its inherent properties, such as compliance and flexibility, provide some promising characteristics for the current robotic applications, including safe human machine interaction, great adaptability to unknown environments and so on. On the contrary, its intrinsic 'soft' characteristic would result in some complex non-linear behaviors, causing more difficulties in deducing kinematic or dynamic models of soft robots than what we often do for rigid robots. In this letter, using Finite Element Method (FEM), we demonstrate a gain-scheduling closed-loop method to control a soft trunk robot operating within its workspace. The main idea of this method is to divide the workspace into several sub-workspaces where the most suitable gains are applied correspondingly in each sub-workspace. As a result, it becomes feasible to control the trunk by gain scheduling when crossing from one sub-workspace to another as well as considering its dynamic characteristics. The derivation of the method is presented accordingly. In the end, the proposed method is validated by experimental testing with convincing results provided afterwards.
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