Abstract

Abstract

Abstract
This paper focus on the classification, in real-time and under uncontrolled lighting, of fabric textures for the automotive industry. Many industrial processes have spatial constraints that limit the effective control of illumination of their vision based systems, hindering their effectiveness. The ability to overcome these problems using robust classification methods with suitable pre-processing techniques and choice of characteristics will increase the efficiency of this type of solutions with obvious production gains and thus economical. For this purpose, this paper studied and analyzed various pre-processing techniques, and selected the most appropriate fabric characteristics for the considered industrial case scenario. The methodology followed was based on the comparison of two different machine learning classifiers, ANN and SVM, using a large set of samples with a large variability of lightning conditions to faithfully simulate the industrial environment. The obtained solution shows the sensibility of ANN over SVM considering the number of features and the size of the training set, showing the better effectiveness and robustness of the last. The characteristics vector uses histogram equalization, Laws filter and Sobel filter, and multi-scale analysis. By using a correlation based method was possible to reduce the number of features used, achieving a better balanced between processing time and classification ratio. © 2016 Springer Science+Business Media New York

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature

Abstract
The brisk and dynamic environment that factories are facing, both as an internal and an external level, requires a collection of handy tools to solve emerging issues in the industry 4.0 context. Part of the common challenges that appear are related to the increasing demand for high adaptability in the organizations' production lines. Mechanical processes are becoming faster and more adjustable to the production diversity in the Fast Moving Consumer Goods (FMCG). Concerning the previous characteristics, future factories can only remain competitive and profitable if they have the ability to quickly adapt all their production resources in response to inconstant market demands. Having previous concerns in focus, this paper presents a fast and adaptative framework for automated cells modeling, simulation and offline robot programming, focused on palletizing operations. Established as an add-on for the Visual Components (VC) 3D manufacturing simulation software, the proposed application allows performing fast layout modeling and automatic offline generation of robot programs. Furthermore, A* based algorithms are used for generating collision-free trajectories, discretized both in the robot joints space and in the Cartesian space. The software evaluation was tested inside the VC simulation world and in the real-world scenario. Results have shown to be concise and accurate, with minor displacement inaccuracies due to differences between the virtual model and the real world. © 2019 IEEE.