Fusing sonars and LRF data to perform SLAM in reduced visibility scenarios

Simultaneous Localization and Mapping (SLAM) approaches have evolved considerably in recent years. However, there are many situations which are not easily handled, such as the case of smoky, dusty, or foggy environments where commonly used range sensors for SLAM are highly disturbed by noise induced in the measurement process by particles of smoke, dust or steam. This work presents a sensor fusion method for range sensing in Simultaneous Localization and Mapping (SLAM) under reduced visibility conditions. The proposed method uses the complementary characteristics between a Laser Range Finder (LRF) and an array of sonars in order to ultimately map smoky environments. The method was validated through experiments in a smoky indoor scenario, and results showed that it is able to adequately cope with induced disturbances, thus decreasing the impact of smoke particles in the mapping task

Special Issue Robótica 2014

This special issue presents extended and revised versions of a selection of papers presented on the IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC-2014), that took place 14-15 May 2014 in the city of Espinho, Portugal.info:eu-repo/semantics/publishedVersio

Arquitetura do agente da equipa de futebol robótico CAMBADA

Mestrado em Engenharia Electrónica e de TelecomunicaçõesThe software agent is the process where all the Artificial Intelligence resides and is responsible for taking high-level decisions. CAMBADA is the robotics soccer team of the IRIS research group, from IEETA, University of Aveiro, that participates in the Middle-Size League of RoboCup. Robotics is an emerging multidisciplinary area that joins computer science, electronics and mechanics and this thesis includes an overview on the general architecture of the CAMBADA robots, from hardware to software, over which all the presented work has been developed. In the competitions context, the reasoning capabilities define the success or the failure of a team. Given the high dynamism of the games, it becomes vital to take the correct decisions, at the right time and in a collaborative way. This thesis intends to improve the structure of the agent, from the code organization to the actual software architecture. A new behavior management model was developed and adopted for the competitions. The constant evolution of the Middle-Size League pushes teams to adapt to new rules each new year. In this context, some novel behaviors were developed and others have been refined in the new architecture. Moreover, for the creation, test and validation of these behaviors, the creation of a series of applications was needed for development, calibration and debugging. The new agent architecture provided a faster and more robust behavior development, and the improvements made on behaviours led to a better global performance of the team in the competitions.O agente de software e o processo onde reside toda a componente de Inteligência Artificial, responsável por tomar as decisões de alto nível. CAMBADA é a equipa de futebol robótico do grupo de investigação IRIS, da unidade de investigação IEETA, da Universidade de Aveiro que participa na Liga dos Robôs Médios do RoboCup. A robótica é uma área multidisciplinar emergente que junta ciências da computação, eletrónica e mecânica e nesta tese está incluída uma explicação geral sobre a arquitetura dos robôs CAMBADA, desde o hardware ao software, sobre os quais foi desenvolvido todo o trabalho apresentado. No contexto de competição, a capacidade de raciocínio é o que define o sucesso ou o insucesso das equipas. Dado o dinamismo atual dos jogos, torna-se vital tomar as decisões corretas, no momento certo e em equipa. Com esta tese pretende melhorar-se a estrutura do agente, desde a organização do código a própria arquitetura de software. Um novo modelo de gestão de comportamentos foi desenvolvido e adotado para as competições. A constante evolução da Liga de Robôs Médios leva as equipas a terem de se adaptar a novas regras todos os anos. Neste contexto, alguns comportamentos foram desenvolvidos de raíz e outros foram melhorados na nova arquitetura. No entanto, para a criação, teste e validação destes comportamentos foi necessária a criação de aplicações de suporte ao desenvolvimento, calibração e de depuração. A nova arquitetura permitiu um desenvolvimento mais rápido e robusto de comportamentos, e os avanços nos comportamentos levaram a uma melhoria considerável no desempenho global da equipa em termos competitivos

Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people