  Particle Swarm Optimisation (PSO) is a powerful optimisation algorithm that
can be used to locate global maxima in a search space. Recent interest in
swarms of Micro Aerial Vehicles (MAVs) begs the question as to whether PSO can
be used as a method to enable real robotic swarms to locate a target goal
point. However, the original PSO algorithm does not take into account
collisions between particles during search. In this paper we propose a novel
algorithm called Force Field Particle Swarm Optimisation (FFPSO) that
designates repellent force fields to particles such that these fields provide
an additional velocity component into the original PSO equations. We compare
the performance of FFPSO with PSO and show that it has the ability to reduce
the number of particle collisions during search to 0 whilst also being able to
locate a target of interest in a similar amount of time. The scalability of the
algorithm is also demonstrated via a set of experiments that considers how the
number of crashes and the time taken to find the goal varies according to swarm
size. Finally, we demonstrate the algorithms applicability on a swarm of real
MAVs.
