Drone Swarms for Animal Monitoring: A Method for Collecting High-Quality Multi-Perspective Data

Drone swarms offer great potential for wildlife monitoring, but their real-world use is still limited. This paper addresses the challenge of deploying drones to collect high-quality, multi-perspective data over herds of gregarious animals. We formalise this problem using the novel concept of surface of interest, combined with a Lambertian-inspired modelling approach. Together, these elements allow us to create an objective function for data quality that also considers the swarm's impact on animal welfare. Using a centralised controller and particle swarm optimisation, our approach determines the drone configurations that maximise this function. Experiments based on real-world animal spatial distributions show that our algorithm effectively identifies these configurations, paving the way for future field tests.