Physicomimetics: Physics-Based Swarm Intelligence

by William M. Spears, Diana F. Spears

Overview

Standard approaches to understanding swarms rely on inspiration from biology and are generally covered by the term “biomimetics”. This book focuses on a different, complementary inspiration, namely physics. The editors have introduced the term 'physicomimetics' to refer to physics-based swarm approaches, which offer two advantages. First, they capture the notion that “nature is lazy', meaning that physics-based systems always perform 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 equations that 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 into 19 chapters as follows. Part I introduces the concept of swarms and offers the reader a physics tutorial; Part II deals with applications of physicomimetics, in order of increased complexity; Part III examines the hardware requirements of the presented algorithms and demonstrates real robot implementations; Part IV demonstrates how the theory can be used to design swarms from first principles and provides a novel algorithm that handles changing environments; finally, Part V shows that physicomimetics can be used for function optimization, moving the reader from issues of swarm robotics to swarm intelligence. The text is supported with a downloadable package containing simulation code and videos of working robots.

This book is suitable for talented high school and undergraduate students, as well as researchers and graduate students in the areas of artificial intelligence and 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