Development of a general purpose tour-guide robot able to learn routes from people and to adapt and move in unstructured and crowded environments

Industrial robots has consolidated its presence in factories and professional environments during the last few years. However, in the next decades it is expected that service robots approach an exponential growth, reaching the mass market and taking over our homes and offices. In order to meet these predictions, there is still some way to go. Service robots need to be able to adapt to the dynamic nature of unstructured environments, where people walk by, and furniture might be moved around. In recent years, tour-guide robots have become a popular research topic because they face many of the challenges which have arisen in service robotics. Essentially, a tour-guide robot must be able to interact with humans, who might demand routes from the robot. Also, in order to showcase these routes, the robot must have a reliable and flexible perception of the changing environment where it operates. For these reasons, the goal of this thesis is to build a general purpose tourguide robot, which should be able to learn routes while following an instructor. The instructor can be anyone, i.e members of the staff from where the robot operates. This makes necessary the development of robust and flexible behaviours. More specifically, we have a) designed and developed a person-following behaviour, b) a human-robot interaction scheme, c) the processes for route recording and reproduction, and d) a strategy to allow the robot to learn its own control algorithms..

Chapter 20 Data Quality and Privacy concerns in Digital Trace Data

"The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

A Robust Face Recognition Algorithm for Real-World Applications

The proposed face recognition algorithm utilizes representation of local facial regions with the DCT. The local representation provides robustness against appearance variations in local regions caused by partial face occlusion or facial expression, whereas utilizing the frequency information provides robustness against changes in illumination. The algorithm also bypasses the facial feature localization step and formulates face alignment as an optimization problem in the classification stage