Catálogo de publicaciones - libros

We present a general-purpose framework for updating a robot’s observation model within the context of planning and execution. Traditional plan execution relies on monitoring plan step transitions through accurate state observations obtained from sensory data. In order to gather meaningful state data from sensors, tedious and time-consuming calibration methods are often required. To address this problem we introduce , a process of learning an observation model through the use of plans composed of scripted actions. The automatically acquired observation models allow the robot to adapt to changes in the environment and robustly execute arbitrary plans. We have fully implemented the method in our AIBO robots, and our empirical results demonstrate its effectiveness.

This paper presents a hybrid behaviour process for performing collaborative tasks and coordination capabilities in a rescue team. RoboCup Rescue simulator and its associated international competition are used as the testbed for our proposal. Unlike other published work in this field one of our main concerns is having good results on RoboCup Rescue championships by emerging behaviour in agents using a biological paradigm. The benefit comes from the hierarchic and parallel organisation of the mammalian brain. In our behaviour process, Artificial Neural Networks are used in order to make agents capable of learning information from the environment. This allows agents to improve several algorithms like their Path Finding Algorithm to find the shortest path between two points. Also, we aim to filter the most important messages that arise from the environment, to make the right choice on the best path planning among many alternatives, in a short time. A policy action was implemented using Kohonen’s network, Dijkstra’s and D* algorithm. This policy has achieved good results in our tests, getting our team classified for RoboCup Rescue Simulation League 2005.

This article presents a constraint-based behavioural architecture for low-level safe navigation, the Survival Kit. Instead of approaching the problem by customising a generic Behaviour-Based architecture, the Survival Kit embodies a dedicated semantics for safe navigation, which augments its expressiveness for the task. An instantiation of the architecture for goal-oriented obstacle avoidance in unstructured indoor environments is proposed. Special attention is given to an environmental feature, the gap, which allows to optimise paths based on immediate ranging data. Experimental results in simulation confirm the capabilities of the approach.

A simple effective method for path planning based on a growing self-organizing elastic neural network, enhanced with a heuristic for the exploration of local directions is presented. The general problem is to find a collision-free path for moving objects among a set of obstacles. A path is represented by an interconnected set of processing units in the elastic self organizing network. The algorithm is initiated with a straight path defined by a small number of processing units between the start and goal positions. The two units at the extremes of the network are static and are located at the start and goal positions, the remaining units are adaptive. Using a local sampling strategy of the points around each processing unit, a Kohonen type learning and a simple processing units growing rule the initial straight path evolves into a collision free path. The proposed algorithm was experimentally tested for 2 DOF and 3 DOF robots on a workspace cluttered with random and non random distributed obstacles. It is shown that with very little computational effort a satisfactory free collision path is calculated.

New results on our artificial landmark recognition approach are presented, as well as new experiments in order to demonstrate the robustness of our method. The objective of our work is the localization and recognition of artificial landmarks to help in the navigation of a mobile robot. Recognition is based on interpretation of histograms obtained from polar coordinates of the landmark symbol. Experiments prove that our approach is fast and robust even if the database has an high number of landmarks to compare with.

Each part of a mobile robot has particular aspects of its own, which must be integrated in order to successfully conclude a specific task. Among these parts, sensing enables to construct a representation of landmarks of the surroundings with the goal of supplying relevant information for the robot’s navigation. The present work describes the architecture of a perception system based on data fusion from a CMOS camera and distance sensors. The aim of the proposed architecture is the spatial location of objects on a soccer field. An SVM is used for both recognition and object location and the process of fusion is made by means of a fuzzy system, using a TSK model.

This paper describes Catrapilas, a small robotic platform, designed to be capable of solving some well known robot problems. Among these are some of the most popular robotic contests, like Micro Mouse, Fire Fighting and Autonomous Driving. It describes the major decisions and details of the physical architecture of the robot, but emphasizes on the high level approach used to control the robotic agent. This approach is based on the creation of a 2D map of the agent’s environment, which should contain all the information needed in order to solve the current problem. There is also a description of the implementation used for the Autonomous Driving Competition, from the 2005 Portuguese National Robotics Festival, and the results that were obtained. There is a focus on the robot’s ability to accomplish the objectives of the contest, and how this proved that the concept and ideas behind Catrapilas are correct.

Multi-Agent Systems (MAS) is now one of the most relevant and attractive research areas in the field of computer science. Since 1993 the area of Multi-Agent Systems/Distributed Artificial Intelligence has been present in the EPIA conferences, both as individual tracks in the main conference and as autonomous workshops.

The (MASTA 2005) took place in the University of Beira Interior, Covilhã, Portugal, December 6-8, 2005, as part of EPIA 2005 – 12th Portuguese Conference on Artificial Intelligence. Focusing on a fundamental area of research in Artificial Intelligence, the 3rd MASTA workshop was the forum for presenting and discussing the most recent and innovative work in the areas of multi-agent systems and autonomous agents.

This paper presents a model of pedagogical negotiation developed for the AMPLIA, an Intelligent Probabilistic Multi-agent Learning Environment. Three intelligent software agents: Domain Agent, Learner Agent and Mediator Agent were developed using Bayesian Networks and Influence Diagrams. The goal of the negotiation model is to increase, as much as possible: (a) the performance of the model the students build; (b) the confidence that teachers and tutors have in the students’ ability to diagnose cases; and the students’ confidence on their own ability to diagnose cases; and (c) the students’ confidence on their own ability to diagnose diseases.

We present a multiagent decision mechanism for the airport traffic control domain. It enables airlines to jointly decide on proposals for plan conflict solutions. The mechanism uses weighted voting for maximizing global utility and Clarke Tax to discourage manipulation. We introduce accounts to ensure that all agents are treated fairly, to some extent. The mechanism allows an airport to determine the pay-off between optimality and fairness of schedules. Also, it compensates for agents that happen to be in practically unfavourable positions.