Kinematic analysis of a novel 3-DOF actuation redundant parallel manipulator using artificial intelligence approach

Authors:
Dan Zhang;Jianhe Lei
Affiliations:
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Ontario, Canada L1H 7K4 and College of Mechanical Engineering, DongHua University, Shanghai 200051, PR Ch ...;Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Ontario, Canada L1H 7K4
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2011

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Abstract

Kinematic analysis is one of the key issues in the research domain of parallel kinematic manipulators. It includes inverse kinematics and forward kinematics. Contrary to a serial manipulator, the inverse kinematics of a parallel manipulator is usually simple and straightforward. However, forward kinematic mapping of a parallel manipulator involves highly coupled nonlinear equations. Therefore, it is more difficult to solve the forward kinematics problem of parallel robots. In this paper, a novel three degrees-of-freedom (DOFs) actuation redundant parallel manipulator is introduced. Different intelligent approaches, which include the Multilayer Perceptron (MLP) neural network, Radial Basis Functions (RBF) neural network, and Support Vector Machine (SVM), are applied to investigate the forward kinematic problem of the robot. Simulation is conducted and the accuracy of the models set up by the different methods is compared in detail. The advantages and the disadvantages of each method are analyzed. It is concluded that @n-SVM with a linear kernel function has the best performance to estimate the forward kinematic mapping of a parallel manipulator.