Classification of Signals by Means of Genetic Programming

[Abstract] This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.Instituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; 10SIN105004P

Estimating the non-linear dynamics of free-flying objects

This paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass. To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing. (C) 2012 Elsevier B.V. All rights reserved

Research on hybrid manufacturing using industrial robot

The applications of using industrial robots in hybrid manufacturing overcome many restrictions of the conventional manufacturing methods, such as small part building size, long building period, and limited material choices. However, some problems such as the uneven distribution of motion accuracy within robot working volume, the acceleration impact of robot under heavy external loads, few methods and facilities for increasing the efficiency of hybrid manufacturing process are still challenging. This dissertation aims to improve the applications of using industrial robot in hybrid manufacturing by addressing following three categories research issues. The first research issue proposed a novel concept view on robot accuracy and stiffness problem, for making the maximum usage of current manufacturing capability of robot system. Based on analyzing the robot forward/inverse kinematic, the angle error sensitivity of different joint and the stiffness matrix properties of robot, new evaluation formulations are established to help finding the best position and orientation to perform a specific trajectory within the robot\u27s working volume. The second research issue focus on the engineering improvements of robotic hybrid manufacturing. By adopting stereo vision, laser scanning technology and curved surface compensation algorithm, it enhances the automation level and adaptiveness of hybrid manufacturing process. The third research issue extends the robotic hybrid manufacturing process to the broader application area. A mini extruder with a variable pitch and progressive diameter screw is developed for large scale robotic deposition. The proposed robotic deposition system could increase the building efficiency and quality for large-size parts. Moreover, the research results of this dissertation can benefit a wide range of industries, such as automation manufacturing, robot design and 3D printing --Abstract, page iv

Интеллектуальные алгоритмы калибровки робототехнических систем : этап 2

В отчете представлены результаты исследований, выполненных по 2 этапу Государственного контракта № П1047 "Интеллектуальные алгоритмы калибровки робототехнических систем" (шифр "НК-291П") от 20 августа 2009 по направлению "Создание интеллектуальных систем навигации и управления" в рамках мероприятия 1.3.2 "Проведение научных исследований целевыми аспирантами.", мероприятия 1.3 "Проведение научных исследований молодыми учеными - кандидатами наук и целевыми аспирантами в научно-образовательных центрах" , направления 1 "Стимулирование закрепления молодежи в сфере науки, образования и высоких технологий" федеральной целевой программы "Научные и научно-педагогические кадры инновационной России" на 2009-2013 годы. Цель работы - поиск интеллектуальных алгоритмов калибровки и управления робототехническими устройствами. Методы систем искусственного интеллекта: нейронные сети и генетические алгоритмы; методы теоретической механики; комплекс распределенных вычислений; робототехнический манипулятор. Разработаны, реализованы и исследованы алгоритмы калибровки манипулятора, основанные на нейронных сетях, проведен их сравнительный анализ, разработаны и реализованы алгоритмы управления манипулятором с учетом калибровочной поправки; получен интеллектуальный алгоритм калибровки многозвенного манипулятора, основанный на новом подходе взаимной работы двух эволюционных алгоритмов, управляющий роботом с целью выполнения оптимального плана калибровки; составлена модель калибровочного эксперимента для произвольной робототехнической системы.ФЦП «Научные и научно-педагогические кадры инновационной России на 2009-2013 годы