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…mehr
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.
Artikelnr. des Verlages: 80075235, 978-3-642-22803-2
2012
Seitenzahl: 676
Erscheinungstermin: 5. Januar 2012
Englisch
Abmessung: 241mm x 160mm x 41mm
Gewicht: 1132g
ISBN-13: 9783642228032
ISBN-10: 3642228038
Artikelnr.: 33681683
Herstellerkennzeichnung
Die Herstellerinformationen sind derzeit nicht verfügbar.
Autorenporträt
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.
Inhaltsangabe
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
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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