Optimization of Tendon-Driven Robot Joint Stiffness using GA-based Learning

• Park Chan Il

  School of Science and Technology, Meiji University

• Kobayashi Hiroaki

  School of Science and Technology, Meiji University

Bibliographic Information

Other Title

• 腱駆動ロボットの作業に適した関節剛性のＧＡによる学習
• ケンクドウ ロボット ノ サギョウ ニ テキシタ カンセツ ゴウセイ ノ GA ニ ヨル ガクシュウ

Abstract

A tendon-driven robot mechanism allows us to control the joint stiffness independantly of the joint torques. Generally, the optimal joint stiffness for desired tasks cannot be found easily. So we adopt a Radial Basis Function Network (RBFN) to describe the trajectory of the stiffness matrix, and modify the parameters using Genetic Algorithm (GA) to find the optimal trajectories. As typical tasks that have impulsive disturbance forces, we choose ball hitting and receiving tasks. The optimal joint stiffness for the hitting task gives the ball the fastest initial speed after the hit, on the other hand, the one for the receiving gives the slowest speed. We use a 2 DOF robotic arm driven with 6 tendons, because we can adjust all of elements of the joint stiffness matrix independently of the joint torques. After modifying conventional GA to fit it for real robot experiments, we make two kinds of experiments given above and show the results.

Journal

• Journal of the Robotics Society of Japan

  Journal of the Robotics Society of Japan 24 (4), 482-488, 2006

  The Robotics Society of Japan

Keywords

• Genetic Algorithm (GA)
• Radial Basis Function Network (RBFN)
• Tendon-Driven
• Elastic
• Redundent
• Robot Arm

Details

• CRID: 1390001204725882112
• NII Article ID: 10017484615
• NII Book ID: AN00141189
• DOI: 10.7210/jrsj.24.482
• ISSN: 18847145
• ISSN: 02891824
• NDL BIB ID: 7963760
• Web Site: http://id.ndl.go.jp/bib/7963760
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7963760
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed