Manfredi Maggiore

Distributed Coordination Theory for Robot Teams develops control algorithms to coordinate the motion of autonomous teams of robots in order to achieve some desired collective goal. It provides novel solutions to foundational coordination problems, including distributed algorithms to make quadrotor helicopters rendezvous and to make ground vehicles move in formation along circles or straight lines. The majority of the algorithms presented in this book can be implemented using on-board cameras. The book begins with an introduction to coordination problems, such as rendezvous of flying robots, and modelling. It then provides a solid theoretical background in basic stability, graph theory and control primitives. The book discusses the algorithmic solutions for numerous distributed control problems, focusing primarily on flying robotics and kinematic unicycles. Finally, the book looks to the future, and suggests areas discussed which could be pursued in further research. This book will provide practitioners, researchers and students in the field of control and robotics new insights in distributed multi-agent systems.

Distributed Coordination Theory for Robot Teams develops control algorithms to coordinate the motion of autonomous teams of robots in order to achieve some desired collective goal. It provides novel solutions to foundational coordination problems, including distributed algorithms to make quadrotor helicopters rendezvous and to make ground vehicles move in formation along circles or straight lines. The majority of the algorithms presented in this book can be implemented using on-board cameras. The book begins with an introduction to coordination problems, such as rendezvous of flying robots, and modelling. It then provides a solid theoretical background in basic stability, graph theory and control primitives. The book discusses the algorithmic solutions for numerous distributed control problems, focusing primarily on flying robotics and kinematic unicycles. Finally, the book looks to the future, and suggests areas discussed which could be pursued in further research. This book will provide practitioners, researchers and students in the field of control and robotics new insights in distributed multi-agent systems.

This brief describes the coordinated control of groups of robots using only sensory input – and no direct external commands. Furthermore, each robot employs the same local strategy, i.e., there are no leaders, and the text also deals with decentralized control, allowing for cases in which no single robot can sense all the others. One can get intuition for the problem from the natural world, for example, flocking birds. How do they achieve and maintain their flying formation? Recognizing their importance as the most basic coordination tasks for mobile robot networks, the brief details flocking and rendezvous. They are shown to be physical illustrations of emergent behaviors with global consensus arising from local interactions. The authors extend the consideration of these fundamental ideas to describe their operation in flying robots and prompt readers to pursue further research in the field. Flocking and Rendezvous in Distributed Robotics will provide graduate students a firm grounding in the subject, while also offering an authoritative reference work for more experienced workers seeking a brief but thorough treatment of an area that has rapidly gained in interest.

A powerful, yet easy-to-use design methodology for the control of nonlinear dynamic systems A key issue in the design of control systems is proving that the resulting closed-loop system is stable, especially in cases of high consequence applications, where process variations or failure could result in unacceptable risk. Adaptive control techniques provide a proven methodology for designing stable controllers for systems that may possess a large amount of uncertainty. At the same time, the benefits of neural networks and fuzzy systems are generating much excitement—and impressive innovations—in almost every engineering discipline. Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques brings together these two different but equally useful approaches to the control of nonlinear systems in order to provide students and practitioners with the background necessary to understand and contribute to this emerging field. The text presents a control methodology that may be verified with mathematical rigor while possessing the flexibility and ease of implementation associated with "intelligent control" approaches. The authors show how these methodologies may be applied to many real-world systems including motor control, aircraft control, industrial automation, and many other challenging nonlinear systems. They provide explicit guidelines to make the design and application of the various techniques a practical and painless process. Design techniques are presented for nonlinear multi-input multi-output (MIMO) systems in state-feedback, output-feedback, continuous or discrete-time, or even decentralized form. To help students and practitioners new to the field grasp and sustain mastery of the material, the book features:*Background material on fuzzy systems and neural networks *Step-by-step controller design *Numerous examples *Case studies using "real world" applications *Homework problems and design projects