Orientador: Jacques Wainer / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-09-11T21:14:07Z (GMT). No. of bitstreams: 1 Pinheiro_PauloGurgel_M.pdf: 1259816 bytes, checksum: a4783df9aa3755becb68ee233ad43e3c (MD5) Previous issue date: 2009 / Resumo: Em um problema de localização multirrobô cooperativa, um grupo de robôs encontra-se em um determinado ambiente, cuja localização exata de cada um dos robôs é desconhecida. Neste cenário, uma distribuição de probabilidades aponta as chances de um robô estar em um determinado estado. É necessário então, que os robôs se movimentem pelo ambiente e gerem novas observações que serão compartilhadas, para calcular novas estimativas. Nos últimos anos, muitos trabalhos têm focado no estudo de técnicas probabilísticas, modelos de comunicação e modelos de detecções, para resolver o problema de localização. No entanto, a movimentação dos robôs é, em geral, definida por ações aleatórias. Ações aleatórias geram observações que podem ser inúteis para a melhoria da estimativa. Este trabalho apresenta uma proposta de localização com suporte a planejamento de ações. O objetivo é apresentar um modelo cujas ações realizadas pelos robôs são definidas por políticas. Escolhendo a melhor ação a ser realizada, é possível receber informações mais úteis dos sensores internos e externos e estimar as posturas mais rapidamente. O modelo proposto, denominado Modelo de Localização Planejada - MLP, utiliza POMDPs para modelar os problemas de localização e algoritmos específicos de geração de políticas. Foi utilizada a localização de Markov como técnica probabilística de localização e implementadas versões de modelos de detecção e propagação de informação. Neste trabalho, um simulador de problemas de localização multirrobô foi desenvolvido, no qual foram realizados experimentos em que o modelo proposto foi comparado a um modelo que não faz uso de planejamento de ações. Os resultados obtidos apontam que o modelo proposto é capaz de estimar as posturas dos robôs com uma menor quantidade de passos, sendo significativamente mais e ciente do que o modelo comparado sem planejamento. / Abstract: In a cooperative multi-robot localization problem, a group of robots is in a certain environment, where the exact location of each robot is unknown. In this scenario, there is only a distribution of probabilities indicating the chance of a robot to be in a particular state. It is necessary for the robots to move in the environment generating new observations, which will be shared to calculate new estimates. Currently, many studies have focused on the study of probabilistic techniques, models of communication and models of detection to solve the localization problem. However, the movement of robots is generally defined by random actions. Random actions generate observations that can be useless for improving the estimate. This work describes a proposal for multi-robot localization with support planning of actions. The objective is to describe a model whose actions performed by robots are defined by policies. Choosing the best action to be performed, the robot gets more useful information from internal and external sensors and estimates the posture more quickly. The proposed model, called Model of Planned Localization - MPL, uses POMDPs to model the problems of location and specific algorithms to generate policies. The Markov localization was used as probabilistic technique of localization and implemented versions of detection models and information propagation model. In this work, a simulator to multi-robot localization problems was developed, in which experiments were performed. The proposed model was compared to a model that does not make use of planning actions. The results showed that the proposed model is able to estimate the positions of robots with lower number of steps, being more e-cient than model compared. / Mestrado / Inteligencia Artificial / Mestre em Ciência da Computação

Trotz der Entwicklungen der letzten Jahre kommt es in der Robotik immer noch vor, dass mobile Roboter scheinbar sinnlose Handlungen ausführen. Der Grund für dieses Verhalten ist oftmals, dass sich das interne Weltbild des Roboters stark von der tatsächlichen Situation, in der sich der Roboter befindet, unterscheidet. Die darauf basierende Robotersteuerung wählt infolge dieser Diskrepanz scheinbar sinnlose Handlungen aus. Eine wichtige Ursache von Lokalisierungsfehlern stellen Kollisionen des Roboters mit anderen Robotern oder seiner Umwelt dar. Mit Hilfe eines Hindernismodells wird der Roboter in die Lage versetzt, Hindernisse zu erkennen, sich ihre Position zu merken und Kollisionen zu vermeiden. Ferner wird in dieser Arbeit eine Erweiterung der Bewegungsmodellierung beschrieben, die die Bewegung in Mobilitätszustände untergliedert, die jeweils ein eigenes Bewegungsmodell besitzen und die mit Hilfe von Propriozeption unterschieden werden können. Mit Hilfe der Servo-Motoren des Roboters lässt sich eine Art Propriozeption erzielen: der momentan gewünschte, angesteuerte Gelenkwinkel wird mit dem tatsächlich erreichten, im Servo-Motor gemessenen Winkel verglichen. Dieser "Sinn" erlaubt eine bessere Beschreibung der Roboterbewegung. Verbesserung des Sensormodells wird das bisher wenig untersuchte Konzept der Negativinformation, d.h. das Ausbleiben einer erwarteten Messung, genutzt. Bestehende Lokalisierungsansätze nutzen diese Information nicht, da es viele Gründe für ein Ausbleiben einer erwarteten Messung gibt. Eine genaue Modellierung des Sensors ermöglicht es jedoch, Negativinformation nutzbar zu machen. Eine Weltmodellierung, die Negativinformation verarbeiten kann, ermöglicht eine Lokalisierung des Roboters in Situationen, in denen einzig auf Landmarken basierende Ansätze scheitern. / Despite the dramatic advancements in the field of robotics, robots still tend to exhibit erratic behavior when facing unexpected situations, causing them, for example, to run into walls. This is mainly the result of the robot''s internal world model no longer being an accurate description of the environment and the robot''s localization within the environment. The key challenge explored in this dissertation is the creation of an internal world model for mobile robots that is more robust and accurate in situations where existing approaches exhibit a tendency to fail. First, means to avoid a major source of localization error - collisions - are investigated. Efficient collision avoidance is achieved by creating a model of free space in the direct vicinity of the robot. The model is based on camera images and serves as a short term memory, enabling the robot to avoid obstacles that are out of sight. It allows the robot to efficiently circumnavigate obstacles. The motion model of the robot is enhanced by integrating proprioceptive information. Since the robot lacks sensors dedicated to proprioception, information about the current state and configuration of the robot''s body is generated by comparing control commands and actual motion of individual joints. This enables the robot to detect collisions with other robots or obstacles and is used as additional information for modeling locomotion. In the context of sensing, the notion of negative information is introduced. Negative information marks the ascertained absence of an expected observation in feature-based localization. This information is not used in previous work on localization because of the several reasons for a sensor to miss a feature, even if the object lies within its sensing range. This information can, however, be put to good use by carefully modeling the sensor. Integrating negative information allows the robot to localize in situations where it cannot do so based on landmark observation alone.