Actions for Shepherding UxVs for Human-Swarm Teaming [electronic resource] : An Artificial Intelligence Approach to Unmanned X Vehicles

Email RIS file
Bookmark
Report an Issue

Shepherding UxVs for Human-Swarm Teaming [electronic resource] : An Artificial Intelligence Approach to Unmanned X Vehicles / edited by Hussein A. Abbass, Robert A. Hunjet

Published
Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition
1st ed. 2021.
Physical Description
XX, 330 p. 88 illus., 44 illus. in color. online resource
Additional Creators
Abbass, Hussein A., Hunjet, Robert A., and SpringerLink (Online service)
Access Online

Availability

I Want It
I Want It

Finding items...

Series
Contents
Introduction -- Introduction to Shepherding -- Introduction to Human-Swarm Teaming -- Swarm Shepherding on Ground -- Swarm Shepherding in Air -- Swarm Shepherding in Air Traffic Control -- Swarm Shepherding in Sea -- Genetic Algorithms for Optimizing Swarm Shepherding -- Reinforcement Learning for Swarm Shepherding -- Learning Classifier Systems for Swarm Shepherding -- Transparent Learning for Swarm Shepherding -- Ontology-guided Learning for Swarm Shepherding -- Mission Planning for Swarm Shepherding -- Real-Time Human Performance Analysis for Human-Swarm Teaming -- Trust for Human-Swarm Teaming -- Machine Education of Smart Shepherds -- The effect of communication range limits on shepherding performance -- Controlling the controllers: the multi shepherd swarm control problem -- Conclusion.
Summary
This book draws inspiration from natural shepherding, whereby a farmer utilizes sheepdogs to herd sheep, to inspire a scalable and inherently human friendly approach to swarm control. The book discusses advanced artificial intelligence (AI) approaches needed to design smart robotic shepherding agents capable of controlling biological swarms or robotic swarms of unmanned vehicles. These smart shepherding agents are described with the techniques applicable to the control of Unmanned X Vehicles (UxVs) including air (unmanned aerial vehicles or UAVs), ground (unmanned ground vehicles or UGVs), underwater (unmanned underwater vehicles or UUVs), and on the surface of water (unmanned surface vehicles or USVs). This book proposes how smart 'shepherds' could be designed and used to guide a swarm of UxVs to achieve a goal while ameliorating typical communication bandwidth issues that arise in the control of multi agent systems. The book covers a wide range of topics ranging from the design of deep reinforcement learning models for shepherding a swarm, transparency in swarm guidance, and ontology-guided learning, to the design of smart swarm guidance methods for shepherding with UGVs and UAVs. The book extends the discussion to human-swarm teaming by looking into the real-time analysis of human data during human-swarm interaction, the concept of trust for human-swarm teaming, and the design of activity recognition systems for shepherding. Presents a comprehensive look at human-swarm teaming; Tackles artificial intelligence techniques for swarm guidance; Provides artificial intelligence techniques for real-time human performance analysis.
Subject(s)
ISBN
9783030608989
Digital File Characteristics
text file PDF
Part Of
Springer Nature eBook
View MARC record | catkey: 33837230