URBANO: A Tour-Guide Robot Learning to Make Better Speeches

—Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create "awareness" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profil

Learning to Be a Good Tour-Guide Robot

Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create "awareness" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profile

Generador Automático de Presentaciones para Robot-Guía

En el ámbito de la robótica, los robots autónomos tienen una importancia cada vez mayor. Bekey los define como intelligent machines capable of performing tasks in the world by themselves, without explicit human control over their actions. l robot Urbano es una plataforma B21r de iRobot, equipada con un sistema de locomoción compuesto por cuatro ruedas, un láser tipo SICK LMS200 montado horizontalmente en la parte superior de la plataforma para la navegación y SLAM, y una cara y brazos robóticos que utiliza para expresar emociones como alegría, tristeza, sorpresa o enfado

A New Cognitive-Based Massive Alarm Management System in Electrical Power Administration

This paper presents a methodology that integrates several available techniques to manage the massive amount of alarm signals in electrical power dispatch control centres, as well as the contribution of each entity involved in the system. Artificial intelligence techniques that can be used in this problem are reviewed here based on the available information. The final objective is to find the root cause of avalanches of alarms (failure tree) and to reduce their number through grouping or clustering techniques so that the EEMUA 191 standards are followed. Even though other contributions in this topic have been made before, the alarm management problem continues to be practically unsolved for many applications in industry. Here, the integration is developed using the ontology of each system domains, i.e., the ontology corresponding to the alarms, controls, events, energy flow and trigger sequence. Additionally, in this methodology, a rule based expert system is used to treat the alarms with a neural net based approach to treat the historical database of alarms and failures

Automatic Presentations Generation Based on Patterns for Tour-Guide Robots

This paper proposes an automatic presentation generation for a tour-guide robot making use of natural language processing and patterns. To reach a suitable approximation in a believable presentation some parameters are identified and described in this paper. Automatic Presentations Generation is based on XML-patterns. The generation process selects information items and obtains the prone of the tree with fuzzy rules

Improving web learning through model optimization using bootstrap for a tour-guide robot

We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability

Decision-Making Mechanism to Organize the Agenda of a Guide-Robot

One of the major challenges in evolutionary robotics is constituted by the need of the robot being able to make decisions on its own, in accordance with the multiple tasks programmed, optimizing its timings and power. In this paper, we present a new automatic decision making mechanism for a robot guide that allows the robot to make the best choice in order to reach its aims, performing its tasks in an optimal way. The election of which is the best alternative is based on a series of criteria and restrictions of the tasks to perform. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality in the decision making. The modeling of the quality index of the best choice to perform is made using fuzzy logic and it represents the beliefs of the robot, which continue to evolve in order to match the "external reality”. This fuzzy system is used to select the most appropriate set of tasks to perform during the day. With this tool, the tour guide-robot prepares its agenda daily, which satisfies the objectives and restrictions, and it identifies the best task to perform at each moment. This work is part of the ARABOT project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create "awareness" in a robot guide

Alarm tree analysis using statistical techniques

This work presents a systematic method for the generation and treatment of the alarms' graphs, being its final object to find the Alarm Root Cause of the Massive Alarms that are produced in the dispatching centers. Although many works about this matter have been already developed, the problem about the alarm management in the industry is still completely unsolved. In this paper, a simple statistic analysis of the historical data base is conducted. The results obtained by the acquisition alarm systems, are used to generate a directed graph from which the more significant alarms are extracted, previously analyzing any possible case in which a great quantity of alarms are produced

New Advances on Modelling Control Processes applied to the Detection of Train Operations

In the main frame of the optimal control process has to be focused this work, where a methodology based on the performance of Genetic Algorithms to be used to search the appropriate knowledge base, defined in the sense of Fuzzy Logic, for the process controller. Two stages have to be considered to obtain the control system - Initial Stage and Conclusions Phase. First point search the control law for the reference functional point and defines design concepts and how to do it works. Second stage search allows adjusting the control action according to the dynamic of the process during its cycle-life

A Fuzzy Logic Inference Approach for the Estimation of the Passengers Flow Demand

This paper presents a new approach that designs the flow of passengers in mass transportation systems in presence of uncertainties. One of the techniques used for the prediction of passenger demand is the origin destination matrices. However, this method is limited to urban areas and rarely to explicit stations. Otherwise, the gravity models based on friction functions can be another alternative; however, it is difficult to fit into practical achievements. Another solution might be the application of artificial intelligence techniques so as to include some intuitive knowledge provided by an expert to predict the flow demand of passengers’ trips in explicit stations. This paper proposes to combine a matrix of origin-destination trips of travel zones, with the intuitive knowledge, applying a fuzzy logic inference approach