Cognitive robotics: a new approach to simultaneous localisation and mapping

Most simultaneous localisation and mapping (SLAM) solutions were developed for navigation of non-cognitive robots. By using a variety of sensors, the distances to walls and other objects are determined, which are then used to generate a map of the environment and to update the robot’s position. When developing a cognitive robot, such a solution is not appropriate since it requires accurate sensors and precise odometry, also lacking fundamental features of cognition such as time and memory. In this paper we present a SLAM solution in which such features are taken into account and integrated. Moreover, this method does not require precise odometry nor accurate ranging sensors.

An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data

Min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) are complementary techniques for analysing short (> 15-25 y), non-stationary, multivariate data sets. We illustrate the two techniques using catch rate (cpue) time-series (1982-2001) for 17 species caught during trawl surveys off Mauritania, with the NAO index, an upwelling index, sea surface temperature, and an index of fishing effort as explanatory variables. Both techniques gave coherent results, the most important common trend being a decrease in cpue during the latter half of the time-series, and the next important being an increase during the first half. A DFA model with SST and UPW as explanatory variables and two common trends gave good fits to most of the cpue time-series. (c) 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.