Algoritmos de aprendizagem para aproximaÃÃo da cinemÃtica inversa de robÃs manipuladores: um estudo comparativo

• Autor(a) principal: Davyd Bandeira de Melo
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFC
• Texto Completo: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=16997
• topic: CinemÃtica; TeleinformÃtica; Redes neurais; MÃquinas - Aprendizagem; Inverse kinematics; Manipulators; Artificial neural networks; Least squares support vector regression; Minimal learning machine; Extreme learning machine; ENGENHARIAS

In this dissertation it is reported the results of a comprehensive comparative study involving seven machine learning algorithms applied to the task of approximating the inverse kinematic model of 3 robotic arms (planar, PUMA 560 and Motoman HP6). The evaluated algorithm are the following ones: Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Least Squares Support Vector Regression (LS-SVR), Minimal Learning Machine (MLM), Gaussian Processes (GP), Adaptive Network-Based Fuzzy Inference Systems (ANFIS) and Local Linear Mapping (LLM). 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.