  The guidance of a large swarm is a challenging control problem. Shepherding
offers one approach to guide a large swarm using a few shepherding agents
(sheepdogs). While noise is an inherent characteristic in many real-world
problems, the impact of noise on shepherding is not a well-studied problem. We
study two forms of noise. First, we evaluate noise in the sensorial information
received by the shepherd about the location of sheep. Second, we evaluate noise
in the ability of the sheepdog to influence sheep due to disturbance forces
occurring during actuation. We study both types of noise in this paper, and
investigate the performance of Str\"{o}mbom's approach under these actuation
and perception noises. To ensure that the parameterisation of the algorithm
creates a stable performance, we need to run a large number of simulations,
while increasing the number of random episodes until stability is achieved. We
then systematically study the impact of sensorial and actuation noise on
performance. Str\"{o}mbom's approach is found to be more sensitive to actuation
noise than perception noise. This implies that it is more important for the
shepherding agent to influence the sheep more accurately by reducing actuation
noise than attempting to reduce noise in its sensors. Moreover, different
levels of noise required different parameterisation for the shepherding agent,
where the threshold needed by an agent to decide whether or not to collect
astray sheep is different for different noise levels.
