Real-Time Automatic Colour Calibration for NAO Humanoids

A challenge in NAO soccer robots is colour calibration. Good colour calibration can result in robust and accurate self-localization of the robot. Currently manual calibration is the only solution, which is used. In this paper, we are proposing an automatic real-time, accurate YUV colour space based colour calibration technique. In order to define average values for the desired colour classes namely orange, white, green and purple, a specified set of frames from the NAO camera are analysed. These average values are corrected by luminance analysis of a new frame and are passed to the K-means clustering algorithm as a set of initial means. In addition to these four values, a set of initial means of the K-means algorithm contains 16 values that are calculated in the following manner: the frame being processed is divided into 4 by 4 grids and the average value from every grid serves as an initial mean for K-means clustering. Consequently, colours of a similar type are combined into clusters. The final step of the proposed technique is cluster classification in which the average values of the desired colour classes are corrected by luminance analysis. As NAO cameras provide video streams in YUV format and the proposed algorithm uses this format there is no need for additional computational steps for conversation between the colour spaces. As a result, computational process is reduced compared to current techniques

Melioration of color calibration, goal detection and self-localization systems of NAO humanoid robots

Selle lõputöö teemaks on autonoomsete robotite jalgpalli tarkvara arendamine.Vaatluse all on teemad nagu värvide kalibreerimine, objetkituvastus ja lokaliseerimine. Uus YUV värviruumi põhine automaatne värvide kalibreerimine on pakutud. Esitatakse detailne kirjeldus automaatse värvide kalibreerimise algoritmi implemenmtreerimisest koos visuaalsete näidetega, mis illustreerivad algoritmi toimimist. Samuti räägitakse täpsemalt muutustest, mis on implementeeritud väravate tuvastamise moodulis ja põhjustest nende muudatuste taga, andes hea ülevaate objekti tuvastamise algoritmi loogikast. Kirjeldatakse hetkel kasutatavat lokaliseerimissüsteemi ja pakutakse välja ning seletatakse lokaliseerimissüsteem parandamise tehnikat.In this thesis, work regarding to autonomous robot soccer software development is presented. The work covers color calibration, object detection and robot localization topics. Novel YUV color space based method for the automation of color calibration is proposed. Detailed description of automatic color calibration technique implementation is provided along with the visual results illustrating performance of the method. Changes implemented to the goal detection module and motivation behind them are described in detail, providing good overview of the logic of the object recognition algorithm. Utilised localisation system is also described and, finally, the localization system enhancement technique is proposed and explained

Artificial Vision in the Nao Humanoid Robot

Projecte Final de Màster UPC realitzat en col.laboració amb l'Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i MatemàtiquesRobocup is an international robotic soccer competition held yearly to promote innovative research and application in robotic intelligence. Nao humanoid robot is the new RoboCup Standard Platform robot. This platform is the new Nao robot designed and manufactured by the french company Aldebaran Robotics. The new robot is an advanced platform for developing new computer vision and robotics methods. This Master Thesis is oriented to the study of some fundamental issues for the artificial vision in the Nao humanoid robots. In particular, color representation models, real-time segmentation techniques, object detection and visual sonar approaches are the computer vision techniques applied to Nao robot in this Master Thesis. Also, Nao’s camera model, mathematical robot kinematic and stereo-vision techniques are studied and developed. This thesis also studies the integration between kinematic model and robot perception model to perform RoboCup soccer games and RoboCup technical challenges. This work is focused in the RoboCup environment but all computer vision and robotics algorithms can be easily extended to another robotics fields

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Desenvolvimento de um sistema de visão para robôs humanoides

Mestrado em Engenharia Electrónica e Telecomunicaçõe

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

YUV based automatic colour calibration for NAO robots

A challenge in real time application of NAO soccer robots is in colour calibration. Many tasks such as localisation and goal detection rely on robustness of colour calibration. In this paper a robust and accurate YUV colour space based automatic colour calibration technique is proposed. First the specific set of frames from the NAO's camera has been analysed in order to define average values for desired colour classes, namely orange, white, green and purple. Then those average values are corrected by a luminance analysis of a new frame and are passed to the K-means clustering algorithm as a set of initial means. Apart from those 4 values, set of initial means of the K-means algorithm also contains 16 values that are calculated in the following manner: the frame currently being processed is divided into 4 by 4 grid and average value from every grid will serve as an initial mean for K-means clustering. After the K-means clustering is applied to the frame so that colours of a similar type are combined into clusters. Final step of the proposed technique is the cluster classification, which is performed by measuring the distance from cluster centroids to the previously calculated average values of desired colour classes corrected by luminance analysis. The proposed colour calibration technique has been tested on white goal detection

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog