A rule based machine learning approach to the nonlinear multifingered robot gripper problem

• Autorzy: Abu-Zitar R. , Al-Fahed Nuseirat A. M.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we present a novel method that utilizes the accumulation of knowledge in a rule base for solving the nonlinear frictional gripper problem for both the isotropic and orthotropic cases. The knowledge is discovered and accumulated in a rule base with the aid of a genetic based machine learning mechanism. This machine learning mechanism extracts rules for solving the problem with the help of the Evolutionary Programming [EP) algorithm. The retrievals are done using the nearest-classifier-algorithm. This approach provides online solutions for the problem, and establishes a dynamic and evolving environment that adapts with new and sudden changes on the grip specifications or on the external forces. The resulting grasping forces using the presented method are compared with grasping forces obtained using other methods, such as the Complementarity Problems. The proposed online method could update the needed grasping forces to keep firm grip if the configuration of the forces externally applied to the object is changed. Numerical examples that illustrate the proposed method are presented.

Słowa kluczowe

EN

robot gripper; nonlinear complementarity problem (NCP); Evolutionary Programming (EP); machine learning; nearest-classifier-algorithm

PL

zacisk robota; programowanie ewolucyjne; komputerowe uczenie się

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2005
• Tom: Vol. 34, no 2
• Strony: 553--573
• Opis fizyczny: Bibliogr. 30 poz., rys., wykr.

Twórcy

autor

Abu-Zitar R.

• Faculty of Information Technology Philadelphia University Amman, Jordan

autor

Al-Fahed Nuseirat A. M.

• Faculty of Engineering Al-Isra Private University Amman, Jordan

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