Abstract
This paper deals with the problem of active target tracking with obstacle avoidance for multi-robot systems. A nonlinear model predictive formation control is presented which uses potential functions as terms of the cost function. These terms penalize the proximity with mates and obstacles, splitting the problem of obstacle avoidance into two repulse functions. Experimental results with real robots are presented to demonstrate the performance of the approach. © IFAC.

Abstract
This article aims to apply the concepts associated with artificial neural networks (ANN) in the control of an autonomous robot system that is intended to be used in competitions of robots. The robot was tested in several arbitrary paths in order to verify its effectiveness. The results show that the robot performed the tasks with success. Moreover, in the case of arbitrary paths the ANN control outperforms other methodologies, such as fuzzy logic control (FLC).

Abstract

Abstract
Cutting and packing problems have been extensively studied in the literature in recent decades, mainly due to their numerous real-world applications while at the same time exhibiting intrinsic computational complexity. However, a major limitation has been the lack of problem generators that can be widely and commonly used by all researchers in their computational experiments. In this paper, a problem generator for every type of two-dimensional rectangular cutting and packing problems is proposed. The problems are defined according to the recent typology for cutting and packing problems proposed by Wascher, Haussner, and Schumann (2007) and the relevant problem parameters are identified. The proposed problem generator can significantly contribute to the quality of the computational experiments run with cutting and packing problems and therefore will help improve the quality of the papers published in this field.

Abstract
The task of moving from one place to another is a difficult challenge that involves obstacle avoidance, staying on street walks, finding doors, knowing the current location and keeping on track through the desired path. Nowadays, navigation systems are widely used to find the correct path, or the quickest, between two places. While assistive technology has contributed to the improvement of the quality of life of people with disabilities, people with visual impairment still face enormous limitations in terms of their mobility. In recent years, several approaches have been made to create systems that allow seamless tracking and navigation both in indoor and outdoor environments. However there is still an enormous lack of availability of information that can be used to assist the navigation of users with visual impairments as well as a lack of sufficient precision in terms of the estimation of the user's location. Blavigator is a navigation system designed to help users with visual impairments. In a known location, the use of object recognition algorithms can provide contextual feedback to the user and even serve as a validator to the positioning module and geographic information system of a navigation system for the visually impaired. This paper proposes a method where the use of computer vision algorithms validate the outputs of the positioning system of the Blavigator prototype. © 2014 Springer International Publishing.

Abstract