An Agent-Based Model of Directional Multi-Herds

Preservation of the existing biodiversity and wildlife is a crucial task for the future of our planet. To be able to protect and conserve animal populations, it is essential to understand their behavior and the factors that influence it. One of the major sources of information for biologists and ethologists is the acquisition of photos and videos from camera traps, Unmanned Aerial Vehicles (UAVs), and Unmanned Ground Vehicles (UGVs). However, appropriately positioning camera traps and optimizing the movement of unmanned devices is difficult, often requiring trial-and-error, and thus amenable to improvement through in-silico simulation. In this context, an appropriate actionable model of the herd behavior of wildlife is of paramount importance, as it can provide a reasonably realistic context for simulating the deployment and control of unmanned devices before field operations. Using ground-truth data from the Kenyan Animal Behavior Recognition (KABR) dataset, we propose a model of directional multi-herds that can be used to simulate the movement of multiple herds of animals. The model and analysis is enriched by an implementation and evaluation into an existing discrete event simulator.