Cooperative chasing and guidance for UAV-based animal rescue algorithms

Abstract Unmanned Aerial Vehicles (UAVs), or drones, bring many opportunities to replace humans in 3D missions: missions which are dull, dirty or dangerous. One example is guiding animals out of dangerous situations, for example close to a forest fire or an accident where toxic chemicals leak into the environment. Since animals do not understand that the drones are intended to help them, they will seek to evade them and this can be used to herd them to safety. Automated shepherding has, to our knowledge, not yet reached the maturity level required to perform such an animal rescue mission. The goal of this paper is to review, select and adapt algorithms in pursuit of this purpose. We examined swarm UAV deployment covering: optimal distribution around the target animal, maintaining it when performing the required task and how to actively evade obstacles en route. This led to the conclusion, that of the presented algorithms, the Inverse Cumulative Density Function (ICDF) is the most suitable for the optimal dispersion of UAVs, while A*, Greedy Best First Search (GBFS) and the potential field algorithm are the most appropriate algorithms to determine suitable route trajectories during such guidance.