Search

The German Workshop on Robotics is a convention of roboticists from academia and industry working on mathematical and algorithmic foundations of robotics, on the design and analysis of robotic systems as well as on robotic applications. Selected contributions from researchers in German-speaking countries as well as from the international robotics community compose this volume. The papers are organized in ten scientific tracks: Kinematic and Dynamic Modeling, Motion Generation, Sensor Integration, Robot Vision, Robot Programming, Humanoid Robots, Grasping, Medical Robotics, Autonomous Helicopters, and Robot Applications. Due to an extensive review and discussion process, this collection of scientific contributions is of very high caliber and promises to strongly influence future robotic research activities.

(Preliminary) The book is a comprehensive collection of the most recent and significant research and applications in the field of fuzzy logic. It covers fuzzy structures, systems, rules, operations as well as important applications, e.g in decision making, environmental prediction and prevention, and communication. It is dedicated to Enric Trillas as an acknowledgement for his pioneering research in the field. The book include a foreword by Lotfi A. Zadeh.

The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments? In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together? John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older'standard Af'. One focus was what Holland called'classifier systems': sets of competing rule­ like'classifiers', each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule'popu­ lation'would explore its available actions and responses, rewarding and rating the active rules accordingly. Then'good'classifiers would be selected and re­ produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.

Genetic Algorithms (GA) as a tool for a search and optimizing methodology has now reached a mature stage. It has found many useful applications in both the scientific and engineering arenas. The main reason for this success is undoubtedly due to the advances that have been made in solid-state microelectronics fabrication that have, in turn, led to the proliferation of widely available, low cost, and speedy computers. The GA works on the Darwinian principle of natural selection for which the noted English philosopher, Herbert Spencer coined the phrase'Survival of the fittest'. As a numerical optimizer, the solutions obtained by the GA are not mathematically oriented. Instead, GA possesses an intrinsic flexibility and the freedom to choose desirable optima according to design specifications. Whether the criteria of concern be nonlinear, constrained, discrete, multimodal, or NP hard, the GA is entirely equal to the challenge. In fact, because of the uniqueness of the evolutionary process and the gene structure of a chromosome, the GA processing mechanism can take the form ofparallelism and multiobjective. These provide an extra dimension for solutions where other techniques may have failed completely. It is, therefore, the aim ofthis booktogather together relevant GA materialthat has already been used and demonstrated in various engineering disciplines.

This volume presents a collection of recent studies covering the spectrum of computational intelligence applications with emphasis on their application to challenging real-world problems. Topics covered include: Intelligent agent-based algorithms, Hybrid intelligent systems, Cognitive and evolutionary robotics, Knowledge-Based Engineering, fuzzy sets and systems, Bioinformatics and Bioengineering, Computational finance and Computational economics, Data mining, Machine learning, and Expert systems.'Computational Intelligence in Optimization'is a comprehensive reference for researchers, practitioners and advanced-level students interested in both the theory and practice of using computational intelligence in real-world applications.

The subject of this book is model abstraction of dynamical systems. The p- mary goal of the work embodied in this book is to design a controller for the mobile robotic car using abstraction. Abstraction provides a means to rep- sent the dynamics of a system using a simpler model while retaining important characteristics of the original system. A second goal of this work is to study the propagation of uncertain initial conditions in the framework of abstraction. The summation of this work is presented in this book. It includes the following: • An overview of the history and current research in mobile robotic control design. • A mathematical review that provides the tools used in this research area. • The development of the robotic car model and both controllers used in the new control design. • A review of abstraction and an extension of these ideas into new system relationship characterizations called traceability and -traceability. • A framework for designing controllers based on abstraction. • An open-loop control design with simulation results. • An investigation of system abstraction with uncertain initial conditions.

Unmanned Aerial Vehicles (UAVs) have seen unprecedented levels of growth in military and civilian application domains. Fixed-wing aircraft, heavier or lighter than air, rotary-wing (rotorcraft, helicopters), vertical take-off and landing (VTOL) unmanned vehicles are being increasingly used in military and civilian domains for surveillance, reconnaissance, mapping, cartography, border patrol, inspection, homeland security, search and rescue, fire detection, agricultural imaging, traffic monitoring, to name just a few application domains. When initially introduced during World War I, UAVs were criticized heavily as being unreliable and inaccurate, and only a handful of people recognized at that early stage their potential and (future) impact on cha- ing the battlefield. To nobody's surprise, about a century later, the total market for UAVs will reach within a few years more than $16 billion, with the US Depa- ment of Defense (DOD) being the champion in funding initiatives, - search and development, as well as procurement. Europe, as a continent, is a very distant second player, expected to spend about €2 billion in research and development, and procurement.

Focuses on acquiring spatial models of physical environments through mobile robots The robotic mapping problem is commonly referred to as SLAM (simultaneous localization and mapping). 3D maps are necessary to avoid collisions with complex obstacles and to self-localize in six degrees of freedom (x-, y-, z-position, roll, yaw and pitch angle) New solutions to the 6D SLAM problem for 3D laser scans are proposed and a wide variety of applications are presented

In this book the use of ER techniques for the design of self-organising group behaviours, for both simulated and real robots is introduced. This research has a twofold value. From an engineering perspective, an automatic methodology for synthesising complex behaviours in a robotic system is described. ER techniques should be used in order to obtain robust and efficient group behaviours based on self-organisation. From a more theoretical point of view, the second important contribution brought forth by the author's experiments concerns the understanding of the basic principles underlying self-organising behaviours and collective intelligence. In this experimental work, the evolved behaviours are analysed in order to uncover the mechanisms that have led to a certain organisation. In summary, this book tries to mediate between two apparently opposed perspectives: engineering and cognitive science. The experiments presented and the results obtained contribute to the assessment of ER not only as a design tool, but also as a methodology for modelling and understanding intelligent adaptive behaviours.

A paradigmatic perspective for modeling and control of physical systems has been used since a long time ago: The input/output approach. Although quite natural for our human experience, this perspective imposes a cause/effect framework to the system under study although such system may not have necessarily such cause/effect structure. Actually, from a general point of view, a system interacts with its environment by exchange of mass and energy which should imply the use of bidirectional arrows in a block diagram rather than using unidirectional ones. Another perspective arose when a new variable showed up in systems and control theory: the state (Kalman). Thus, the input/state/output approach was born. Although the concept of state is cornerstone, qualitative characteristics of a system (stability, controllability and observability) have to be defined in terms of a representation of the system, i.e., such characteristics become representation dependent. In contrast, the relatively new Behavioral Approach for systems and control (Jan C. Willems) deals directly with the solution of the differential equations which represent the system. This set of allowed solutions is referred to as the behavior of the system. Thus, the key is the behavior and not the representation. This fact makes this perspective a representation free approach. This book reviews known topics of the Behavioral Approach and offers new theoretic results with the advantage of including control algorithms implemented numerically in the computer. In addition, issues of numerical analysis are also included. The programs and algorithms are MATLAB based.

Cells and Robots is an outcome of the multidisciplinary research extending over Biology, Robotics and Hybrid Systems Theory. It is inspired by modeling reactive behavior of the immune system cell population, where each cell is considered as an independent agent. In our modeling approach, there is no difference if the cells are naturally or artificially created agents, such as robots. This appears even more evident when we introduce a case study concerning a large-size robotic population scenario. Under this scenario, we also formulate the optimal control of maximizing the probability of robotic presence in a given region and discuss the application of the Minimum Principle for partial differential equations to this problem. Simultaneous consideration of cell and robotic populations is of mutual benefit for Biology and Robotics, as well as for the general understanding of multi-agent system dynamics. The text of this monograph is based on the PhD thesis of the first author. The work was a runner-up for the fifth edition of the Georges Giralt Award for the best European PhD thesis in Robotics, annually awarded by the European Robotics Research Network (EURON).

Geometric Fundamentals of Robotics provides an elegant introduction to the geometric concepts that are important to applications in robotics. This second edition is still unique in providing a deep understanding of the subject: rather than focusing on computational results in kinematics and robotics, it includes significant state-of-the-art material that reflects important advances in the field, connecting robotics back to mathematical fundamentals in group theory and geometry. Geometric Fundamentals of Robotics serves a wide audience of graduate students as well as researchers in a variety of areas, notably mechanical engineering, computer science, and applied mathematics. It is also an invaluable reference text.

Petri nets do not designate a single modeling formalism. In fact, newcomers to the field confess sometimes to be a little puzzled by the diversity of formalisms that are recognized under this'umbrella'. Disregarding some extensions to the theoretical modeling capabilities, and looking at the level of abstraction of the formalisms, Condition/Event, Elementary, Place/Transition, Predicate/Transition, Colored, Object Oriented... net systems are frequently encountered in the literature. On the other side, provided with appropriate interpretative extensions, Controled Net Systems, Marking Diagrams (the Petri net generalization of State Diagrams), or the many-many variants in which time can be explicitly incorporated -Time(d), Deterministic, (Generalized) Stochastic, Fuzzy...- are defined. This represents another way to define practical formalisms that can be obtained by the'cro- product'of the two mentioned dimensions. Thus Petri nets constitute a modeling paradigm, understandable in a broad sense as'the total pattern of perceiving, conceptualising, acting, validating and valuing associated with a particular image of reality that prevails in a science or a branch of science'(Thomas S. Kuhn).