Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

How to Do Things With Words

Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.

http://www.gbv.de/dms/hebis-darmstadt/toc/120387220.pdf

Introduction to Autonomous Mobile Robots

The Study of Instinct

생체의 모델링과 Simulation

The vision of an ecology of physically embedded intelligent systems, or PEIS-Ecology, combines insights from the fields of autonomous robotics and ambient intelligence to provide a new approach to building robotic systems in the service of people. In this paper, we present this vision, and we report the results of a four-year collaborative research project between Sweden and Korea aimed at the concrete realization of this vision.We focus in particular on three results: a robotic middleware able to cope with highly heterogeneous systems; a technique for autonomous self-configuration and reconfiguration; and a study of the problem of sharing information of both physical and digital nature.

The PEIS-Ecology project: Vision and results

This paper deals with target tracking control of unicycle type mobile robots. Target tracking function is essential for autonomous robots such as guide robots, security guard robots, etc. In the field of mobile robot control, many control schemes for posture stabilization and trajectory tracking problem have been proposed. Target tracking control, however, cannot be achieved using these kinds of control laws. Therefore, a new global asymptotic stable controller for this problem is designed using the backstepping method. The stability of the system is proved using the Lyapunov function. Various simulation results validate the performance and theoretical analysis.

A stable target-tracking control for unicycle mobile robots

A URC (ubiquitous robotic companion) is a concept for a network-based service robot. It allows the service robot to extend its functions and services by utilizing external sensor networks and remote computing servers. It also provides the robot's services at any time and any place. The URC requires not only the hardware infrastructure such as ubiquitous networks or sensor networks and high-performance computing servers but also the software infrastructure which resides above the hardware infrastructure. In this paper, authors introduce the CAMUS (context-aware middleware for URC system) as a part of the software infrastructure, which is a system middleware to support context-aware services for network-based robots. The CAMUS is based on the CORBA technology. It provides the common data model for different types of context information from external sensors, applications and users in the environment. It also offers the software framework to acquire, interpret and disseminate context information. PLUE (Programming Language for Ubiquitous Environment) is proposed to describe context-aware services for robots.

CAMUS: a middleware supporting context-aware services for network-based robots

In realizing a user recognition system and a method thereof, a user feature vector is extracted from an input facial image, and at least one cluster is generated and a user feature template is enrolled. The cluster includes the feature vector as a member of it. When a user facial image is inputted, a user feature vector is extracted from the image, and a similarity between the feature vector and the user feature template is calculated. When the similarity is greater than a predetermined threshold value, a user of the user feature template is recognized as the user of the input image.

User recognition system and method thereof

We introduce the concept of ecology of physically embedded intelligent systems, or PEIS-ecology. This is a network of heterogeneous robotic devices (PEIS) pervasively embedded in the environment. A PEIS can be as simple as a toaster and as complex as a humanoid robot. PEIS can exchange information at different levels of abstraction, and share both physical and virtual functionalities to perform complex tasks. By putting together insights from the fields of autonomous robotics and of ambient intelligence, the PEIS-ecology approach explores a new road to building assistive, personal, and service robots. In this paper, we discuss this concept, describe a first realization of it, and show an implemented use-case scenario

Young-Jo Cho

Papers

The PEIS-Ecology project: Vision and results

A stable target-tracking control for unicycle mobile robots

CAMUS: a middleware supporting context-aware services for network-based robots

User recognition system and method thereof

An ecological approach to odour recognition in intelligent environments