Optimizing Cooperative Cognitive Search and Rescue UAVs

A need exists for a self-forming, self-organizing, cognitive, cooperative, automated unmanned aerial vehicle (UAV) network system to more efficiently perform UAV-based maritime search and rescue (SAR) operations. Although current search patterns (e.g., traditional lawn mower methods) are thorough, they result in too much time spent searching low-probability areas. This decreases the chances of a successful rescue and increases the risk of lost recovery opportunities (e.g., death due to hypothermia in the case of human search targets). Our goal is to optimize UAV-based SAR operations. As directed by an onboard computer, UAVs would fly coordinated search patterns based on the target's last known position and the direction and speed of winds and currents. By enabling the UAVs to act collectively and cooperatively, we can enhance the efficiency and effectiveness of a multi-UAV network's SAR mission. To achieve this, we applied cooperative game theory as an enabling function in the development of a cognitive system encompassing multiple vehicles. Based on simulations, we showed that an optimal dynamic search pattern and vehicle positioning strategy can be realized using decision algorithms based on elements of game theory.