Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence

Visualisation of Invisible Hazardous Substances Using Unicellular Swarm Intelligence

  • John Oyekan

Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 14)

Table of contents

  1. Front Matter
    Pages i-x
  2. John Oluwagbemiga Oyekan
    Pages 1-10
  3. John Oluwagbemiga Oyekan
    Pages 11-66
  4. John Oluwagbemiga Oyekan
    Pages 67-76
  5. John Oluwagbemiga Oyekan
    Pages 77-109
  6. John Oluwagbemiga Oyekan
    Pages 129-161
  7. John Oluwagbemiga Oyekan
    Pages 163-185
  8. John Oluwagbemiga Oyekan
    Pages 187-194

About this book

Introduction

The book discusses new algorithms capable of searching for, tracking, mapping and providing a visualization of invisible substances. It reports on the realization of a bacterium-inspired robotic controller that can be used by an agent to search for any environmental spatial function such as temperature or pollution. Using the parameters of a mathematical model, the book shows that it is possible to control the exploration, exploitation and sensitivity of the agent. This feature sets the work apart from the usual method of applying the bacterium behavior to robotic agents. The book also discusses how a computationally tractable multi-agent robotic controller was developed and used to track as well as provide a visual map of a spatio-temporal distribution of a substance. On the one hand, this book provides biologists and ecologists with a basis to perform simulations related to how individual organisms respond to spatio-temporal factors in their environment as well as predict and analyze the behavior of organisms at a population level. On the other hand, it offers robotic engineers practical and fresh insights into the development of computationally tractable algorithms for spatial exploratory and mapping robots. It also allows a more general audience to gain an understanding of the design of computational intelligence algorithms for autonomous physical systems.

Keywords

Multi-Agent Algorithms Bacteria Controller Flocking Controller Self-Organizing Swarm Flocking Behavior Swarm Of Uavs Pollution Monitoring Complex Spatiotemporal Profiles Invisible Spatiotemporal Quantities Dynamic Sensors Mobile Robots

Authors and affiliations

  • John Oyekan
    • 1
  1. 1.School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-27425-6
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-27423-2
  • Online ISBN 978-3-319-27425-6
  • Series Print ISSN 2195-3562
  • Series Online ISSN 2195-3570
  • About this book
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