This paper deals with Reconfigurable Cable-Driven Parallel Robots (RCDPRs). A RCDPR is able to change the locations of its cable exit points, the latter being defined as the connection points between the cables and the robot base frame. Given a RCDPR, a set of possible reconfigurations, a desired platform path and a description of the robot environment, the reconfiguration strategy proposed in this paper selects the optimal configurations to be associated to each point of the desired path. The selection of the optimal configurations can be performed with respect to several criteria such as the number of configuration changes, the number of cable reconfigurations and the robot stiffness. In this paper, the optimization is performed using a Dijkstra's based algorithm.

This paper presents the preliminary studies dedicated to the design of cable-driven parallel robots (CDPRs) for industrial purposes. The goal is to transport the proper tools around a jacket, an offshore structure supporting a wind turbine, in order to perform painting and sandblasting tasks. In this paper, a simplified case study consisting of a structure composed of four tubes is investigated. A fully constrained CDPR and a suspended CDPR are studied. The design problems of the CDPRs at hand are formulated as optimization problems. They aim at determining the locations of the base anchor points of the cables that minimize the size of the CDPR, while satisfying a set of constraints. Those constraints guarantee that the moving platform can support the external wrenches and that there is no interference between the cables and between the cables and the environment, all along the path to be followed by the moving platform.

The objective of this paper is to describe an algorithm able to solve the traffic sign recognition problem, based on a structure composed by a cascade of competing classifiers and some computer vision pre-processing operations. Traffic sign recognition is a very complex problem, involving a multiclass analysis with unbalanced class frequencies, most of them very similar to each other. With our system, that we are going to call Traffic Sign Classifier (TSC), during the competition promoted by the Institut für Neuroinformatik, Ruhr Universität Bochum, it was possible to recognize more than 40 classes of signs with an average error close to 3%. The algorithm, realized by our development team, consists basically of two modules: a preprocessing module, where the data are managed in order to extract some features, such as the Hue Histogram (HH) and the Histograms of Oriented Gradients (HOG); a second module, where the data coming from the first one are analyzed using a sequence of Support Vector Machines (SVM), implemented with the One Versus All (OVA) methodology. This module includes a couple of systems, composed of several SVMs; one of these systems consists of a hierarchical structure. The results coming out from both the systems are compared with each other in order to define which is the most reliable. This work is performed by the so called “Combining the Results and Assigning the Labels” procedure; calibrating the systems and the parameters employed inside the several analyses performed, it is possible to decrease the number of misclassifications and consequently increase the performance of the entire network.