Physicomimetics: Physics-Based Swarm Intelligence

Physicomimetics: Physics-Based Swarm Intelligence

Paperback(2012)

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Overview

Physicomimetics: Physics-Based Swarm Intelligence by William M. Spears

Standard approaches to understanding swarms relyon inspiration from biology and are generally coveredby the term “biomimetics”. This book focuses on adifferent, complementary inspiration, namely physics.The editors have introduced the term 'physicomimetics'to refer to physics-based swarm approaches, which offertwo advantages. First, they capture the notion that“nature is lazy', meaning that physics-based systems alwaysperform the minimal amount of work necessary, which is an especially important advantage in swarm robotics.Second, physics is the most predictive science, and can reduce complex systems to simple concepts and equationsthat codify emergent behavior and help us to design and understand swarms.

The editors consolidated over a decade of work on swarm intelligence and swarm robotics, organizing the book into19 chapters as follows. Part I introduces the concept ofswarms and offers the reader a physics tutorial; Part IIdeals with applications of physicomimetics, in order ofincreased complexity; Part III examines the hardwarerequirements of the presented algorithms and demonstrates real robot implementations; Part IV demonstrates how thetheory can be used to design swarms from first principlesand provides a novel algorithm that handles changingenvironments; finally, Part V shows that physicomimeticscan be used for function optimization, moving the readerfrom issues of swarm robotics to swarm intelligence. Thetext is supported with a downloadable package containingsimulation code and videos of working robots.

This book is suitable for talented high school and undergraduate students, as well as researchers andgraduate students in the areas of artificial intelligenceand robotics.

Product Details

ISBN-13: 9783642448638
Publisher: Springer Berlin Heidelberg
Publication date: 02/23/2014
Edition description: 2012
Pages: 646
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Dr. William Spears is the CEO of Swarmotics LLC, a company that provides consulting expertise in distributed agents, sensing networks, artificial intelligence, machine learning, optimization, and swarm robotics; he was formerly a professor in the Dept. of Computer Science at the University of Wyoming, Laramie. Dr. Diana Spears was a professor in the Dept. of Computer Science at the University of Wyoming, Laramie, and is currently a director of Swarmotics, LLC.

Table of Contents

Part I- Introduction

Chap. 1 - Nature Is Lazy

Chap. 2 - NetLogo and Physics

Chap. 3 - NetLogo and Physicomimetics

Chap. 4 - Pushing the Envelope

Part II - Robotic Swarm Applications

Chap. 5 - Local Oriented Potential Fields: Self-deployment and Coordination of an Assembling Swarm of Robots

Chap. 6 - Physicomimetics for Distributed Control of Mobile Aquatic Sensor Networks in Bioluminescent Environments

Chap. 7 - Gas-Mimetic Swarms for Surveillance and Obstacle Avoidance

Chap. 8 - A Multirobot Chemical Source Localization Strategy Based on Fluid Physics: Theoretical Principles

Chap. 9 - A Multirrobot Chemical Source Localization Strategy Based on Fluid Physics: Experimental Results

Part III - Physicomimetics on Hardware Robots

Chap. 10 - What Is a Maxelbot?

Chap. 11 - Uniform Coverage

Chap. 12 - Chain Formations

Chap. 13 - Physicomimetic Motion Control of Physically Constrained Agents

Part IV - Prediction, Adaptation, and Swarm Engineering

Chap. 14 - Adaptive Learning by Robot Swarms in Unfamiliar Environments

Chap. 15 - A Statistical Framework for Estimating the Success Rate of Liquid-Mimetic Swarms

Chap. 16 - Physicomimetic Swarm Design Considerations: Modularity, Scalability, Heterogeneity, and the Prediction Versus Control Dilemma

Chap. 17 - Using Swarm Engineering to Design Physicomimetic Swarms

Part V - Function Optimization

Chap. 18 - Artificial Physics Optimization Algorithm for Global Optimization

Chap. 19 - Artificial Physics for Noisy Nonstationary Environments

App. A - Anomalous Behavior of the Random Number Generator

Index

References

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