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.
