# Fly Safe: Aerial Swarm Robotics using Force Field Particle Swarm Optimisation

## TL;DR

This paper introduces FFPSO, a novel swarm optimization algorithm that reduces collisions among aerial robots while effectively locating targets, demonstrated through simulations and real MAV experiments.

## Contribution

The paper presents FFPSO, an innovative extension of PSO incorporating repellent forces to prevent collisions in aerial swarms, with validation on real MAVs.

## Key findings

- FFPSO eliminates particle collisions during search.
- FFPSO finds targets in similar time as standard PSO.
- Scalability demonstrated with varying swarm sizes.

## Abstract

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.