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Smart Shepherding: Towards Transparent Artificial Intelligence Enabled Human-Swarm Teams Read chapter Read first chapter

2021 | OriginalPaper | Chapter

10. Apprenticeship Bootstrapping Reinforcement Learning for Sky Shepherding of a Ground Swarm in Gazebo

Authors : Hung Nguyen, Matthew Garratt, Hussein A. Abbass

Published in: Shepherding UxVs for Human-Swarm Teaming

Publisher: Springer International Publishing

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Abstract

The coordination of unmanned air–ground vehicles has been an active area due to the significant advantages of this coordination wherein unmanned air vehicles (UAVs) have a wide field of view, enabling them to effectively guide a swarm of unmanned ground vehicles (UGVs). Due to significant recent advances in artificial intelligence (AI), autonomous agents are being used to design more robust coordination of air–ground systems, reducing the intervention load of human operators and increasing the autonomy of unmanned air–ground systems. A guidance and control shepherding system design allows for single learning agent to influence and manage a larger swarm of rule-based entities. In this chapter, we present a learning algorithm for a sky shepherd-guiding rule-based AI-driven UGVs. The apprenticeship bootstrapping learning algorithm is introduced and is applied to the aerial shepherding task.

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previous chapter Sky Shepherds: A Tale of a UAV and Sheep
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Metadata
Title
Apprenticeship Bootstrapping Reinforcement Learning for Sky Shepherding of a Ground Swarm in Gazebo
Authors
Hung Nguyen
Matthew Garratt
Hussein A. Abbass
Copyright Year
2021
Book
Shepherding UxVs for Human-Swarm Teaming

Print ISBN: 978-3-030-60897-2

Electronic ISBN: 978-3-030-60898-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-60898-9_10

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