Design, Modeling and Optimal Control Strategy for an Under-Actuated Anthropomorphic Robotic Finger

Wonseok Jeon, Kyungchan Son, Jaegak Lee, Hyemi Jeong, Hyunseok Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we describe the design and optimal control strategy of an under-actuated, anthropomorphic robotic finger. A compliance element (silicon rubber skin) and series elastic actuators are incorporated in the design to reduce the number of actuators, and for robotic finger control. Manufacturing of the robotic finger is further simplified by the basic joint design and a 3D printing technique. The proposed metacarpophalangeal (MCP) joint structure allows flexion-extension and adduction-abduction motions of the robotic finger to be controlled easily and independently. However, due to the under-actuated system, and joint stiffness caused by the compliance element, the design is limited by inverse kinematics issues, preventing some of the desired joint angles from being achieved. We analyzed this problem and proposed an optimal control strategy to address this issue. Our experimental results showed that good performance of the optimal control strategy of the under-actuated anthropomorphic robotic finger prototype.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages39-44
Number of pages6
ISBN (Electronic)9781728103761
DOIs
Publication statusPublished - 2018 Jul 2
Event2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018 - Kuala Lumpur, Malaysia
Duration: 2018 Dec 122018 Dec 15

Publication series

Name2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018

Conference

Conference2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018
Country/TerritoryMalaysia
CityKuala Lumpur
Period18/12/1218/12/15

Bibliographical note

Funding Information:
*This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07049267). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (2018R1A4A1025986) and the Technology Innovation Program (10073129, Development of driving and manipulation intelligence based on deep learning and inverse reinforcement learning for dual arm mobile robot) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea) †Corresponding author 1Department of Mechanical Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Korea. {wsjeonno, skc0313, jaegak, stree104, hsyang}@yonsei.ac.kr 2Center for Information Storage Devices (CISD), Yonsei University, Seodaemun-gu, Seoul 03722, Korea.

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Artificial Intelligence
  • Human-Computer Interaction

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