Autonomous and Collective Intelligence for UAV Swarm in Target Search Scenario

Unmanned Aerial Vehicle (UAV) swarm, also named drone swarm, has been the study object of many types of research due to its potential to improve applications such as monitoring, surveillance, and search missions. With several drones flying simultaneously, the challenge is to increase their level of automation and intelligence while avoiding collision, reducing communication level with these entities, and improving strategical organization to accomplish a specific task. In this sense, we propose a solution to coordinate a UAV swarm using bivariate potential fields with autonomous and distributed intelligence among drones for a cooperative target search application. Results have shown an improvement in the swarm effectiveness by reducing the number of UAVs blocked at local minima by using distributed decision-making methods, proving to be an effective approach to solve this frequent problem in potential fields.