Open AccessSIMULATION AND ANALYZES OF INVERSE-KINEMATIC MODEL OF HUMANOID ROBOT HAND
Author(s) -
Martin Varga,
Filip Filakovský,
Ivan Virgala
Publication year - 2019
Publication title -
tehnìčnì nauki ta tehnologìï
Language(s) - English
Resource type - Journals
eISSN - 2519-4569
pISSN - 2411-5363
DOI - 10.25140/2411-5363-2019-3(17)-117-122
Subject(s) - inverse kinematics , humanoid robot , jacobian matrix and determinant , kinematics , computer science , forward kinematics , robotics , mechatronics , robot , artificial intelligence , matlab , control engineering , field (mathematics) , stability (learning theory) , robot kinematics , software , engineering , machine learning , mathematics , mobile robot , programming language , physics , classical mechanics , pure mathematics , operating system
Urgency of the research. Nowadays robotics and mechatronics come to be mainstream. With development in these areas also grow computing fastidiousness. Since there is significant focus on numerical modeling and algorithmization in kinematic and dynamic modeling. Target setting. Suitable approach for numerical modeling is important from the view of time consumption as well as stability of computing. Actual scientific researches and issues analysis. Designing and modeling of humanoid robots have high interest in the field of robotics. The hardware and mechanical design of robots is on significantly higher level in comparison with software of robots. So, modeling and control of robots is in the interest of researchers. Uninvestigated parts of general matters defining. Comparison of methods for numerical modeling of inverse kinematics.The research objective. Comparing four methods from the view of performance and stability. The statement of basic materials. This paper investigates the area of kinematic modeling of humanoid robot hand and simulation in MATLAB. Conclusions. The paper investigated inverse kinematic model approaches. There were analyzed pseudoinverse method, transpose of Jacobian method, damped least squares method as an optimization method. The results of the simulations show the advantages of optimization method. During the simulations it never fail in comparison with other tested methods.