Description: The utilization of Unmanned Aerial Vehicles (UAVs) in search missions has many advantages, including maneuverability, human risk reduction, cost effectiveness of equipment, and the application of search control algorithms. The latter provides aspace to develop and use state-of-the-art methods which can greatly increase the performance of the search when compared to conventional approaches that employ simple and usually predefined trajectories. Furthermore, when searching for a non-stationary target, such as a person floating in the sea, the consideration of its movement dynamics is critical to search success. When coupled with vision sensing, detection and tracking systems that are already available, UAV search systems can serve as the basis for the development of affordable practical search technology. We propose to test the applicability of a novel area coverage method and target detection and sensing system on the problem of searching in oceanic environments. For the area coverage method we intend to use HEDAC (Heat Equation Driven Area Coverage) method which was developed in our team and has shown to be one of the most advanced algorithms for heterogeneous multi-agent control in unsteady conditions. HEDAC ensures that non-uniform, unsteady goal area coverage is achieved by performing an action along the trajectories of multiple agents. As for the search agents, we plan to use multiple UAVs with heterogeneous sensing equipment and flight parameters (principally velocity). These will be rotary-wing UAVs controlled with the customized flight controller and equipped visual and infrared spectrum cameras. Visual detection and robust tracking of targets (humans and buoy GPS drifters) can be accomplished by making use of supervised or semi-unsupervised deep convolutional and recurrent neural networks. Multi-agent systems are a dynamic and growing research area with influence on many other disciplines of science and technology. The control of such systems is one of the most important and intriguing problems which arise in different forms depending on the application. One such control problem is a multi-agent area coverage control, which relates closely to many real-world applications in search and surveillance technology. The proposed methodology for oceanic search and rescue offers a state-of-the art breakthrough in modern multi-agent applications. In contrast to similar attempts of the research in this field, HEDAC control algorithm offers robustness, flexibility and efficiency built upon a rather simple underlying ideas. Further advances in HEDAC multi-agent control would serve as significant improvement in search theory and methodology, but also in multi-agent control in general. Advancement in search control could potentially be beneficial in other fields of engineering, natural or even social sciences. For example, animal population overseeing and tracking for the purpose of recording and analyzing the movement dynamics in specific areas of interest, such as national parks, wilderness, open space cattle farming. Consideration of other forms of non-stationary targets such as simple human movement estimation in crowds and public gatherings, could bring enhancements in overall public security. This research offers several state-of-the-art topics, related to artificial intelligence and autonomous robotic systems, which are highly fruitful areas in modern science. It is justified to believe that all members of the team and associates will benefit from the proposed research, with developing skills and expertise in highly prevailing field and setting fundamentals for their own careers.