The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	An artificial neural network for redundant robot inverse kinematics computation
Author:	Utama, Wibawa
View Online:	njit-etd1990-013 ([vi], [85] pages ~ 3.4 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Master of Science
Program:	Electrical Engineering
Document Type:	Thesis
Advisory Committee:	Hou, Edwin (Committee chair) Robbi, Anthony D. (Committee member) Ansari, Nirwan (Committee member)
Date:	1990
Keywords:	Neural networks (Computer science) Manipulators (Mechanism) Robots, Industrial. Kinematics
Availability:	Unrestricted
Abstract:	A redundant manipulator can be defined as a manipulator that has more degrees of freedom than necessary to determine the position and orientation of the end effector. Such a manipulator has dexterity, flexibility, and the ability to maneuver in presence of obstacles. One important and necessary step in utilizing a redundant robot is to relate the joint coordinates of the manipulator with the position and orientation of the end-effector. This specification is termed as the direct kinematics problem and can be written as x = f(q) where x is a vector representing the position and orientation of the end-effector, q is the Joint vector, and f is a continuous non-linear function defined by the design of the manipulator. The inverse kinematics problem can be stated as: Given a position and orientation of the end-effector, determine the joint vector that specifies this position a q = f -1(x). and orientation. That is, For non-trivial designs, f -1 cannot be expressed analytically. This paper presents a solution to the inverse kinematics problem for a redundant robot based on multilayer feed-forward artificial neural network.