Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre
of Influence Measures
Abstract
Boids (bird-oids) is a widely used model to mimic the behaviour of
birds. Shoids (sheep-oids) rely on the same boids rules with the
addition of a repulsive force away from a sheepdog (a herding agent).
Previous work assumed homogeneous shoids. Real-world observations of
sheep show non-homogeneous responses to the presence of a herding agent.
We present a portfolio of information-theoretic and spatial indicators
to track the footprints of shoids with different parameters within the
shoid flock. The portfolio is named the Centre of Influence to indicate
that the aim is to identify the influential shoids with the highest
impact on flock dynamics. We use both synthetic simulation-driven data
and measurements collected from live sheep herding trials by an unmanned
aerial vehicle (UAV) to validate the proposed measures. The resultant
measures will allow us in our future research to design more efficient
control strategies for the UAV, by polarising the attention of the
machine learning algorithm on those shoids with influence footprints, to
drive the flock to improve the herding of sheep.