Modelos locais para aproximação da cinemática inversa de robôs redundantes: um estudo comparativo

Abstract

In this dissertation it is reported the results of a comprehensive comparative study involving six local models applied to the task of learning the inverse kinematics of three redundant robotic arm (planar, PUMA 560 and Motoman HP6). The evaluated algorithms are the following ones: radial basis functions network (RBFN), local model network (LMN), SOMbased local linear mapping (LLM), local linear mapping over k-winners (K-SOM), local weighted regression (LWR) and counter propagation (CP). Each algorithm is evaluated with respect to its accuracy in estimating the joint angles given the cartesian coordinates which comprise end-effector trajectories within the robot workspace. A comprehensive evaluation of the performances of the aforementioned algorithms is carried out based on correlation analysis of the residuals. Finally, hypothesis testing procedures are also executed in order to verifying if there are significant differences in performance among the best algorithms.