Probability Maps and Search Strategies for Automated UAV Search in the Wadden Sea

Search and rescue (SAR) operations with unmanned aerial vehicles (UAVs) have been the subject of numerous scientific studies. Their effectiveness relies on intelligent and efficient path planning. Not only can they save expensive resources, they can minimize potential risks for the rescue team. This paper deals with optimal path planning for automated UAV-SAR operations, tailored specifically to the challenging inter-tidal environment of the Wadden Sea. The aim is to minimize the search time and maximize the discovery probability of lost persons (LP) with intelligent UAV path-planning strategies. To achieve this, first a dynamic probability map (PM) of the lost person’s possible location is generated. Two distinct methods are evaluated for this purpose: Monte Carlo Simulations (MCS) and a more efficient Markov Chain (MAC) model. The PM then directly informs the UAV’s decision making process. Then, several automated search strategies are systematically evaluated and compared in a comprehensive simulation study, from simple coverage patterns to advanced PM-driven algorithms. MAC-generated PMs prove to provide a fast and reliable foundation for time-critical applications such as SAR operations. Additionally, PM-based search strategies outperform standard search patterns, especially in larger search regions. Furthermore, the evaluation is extended to multi-UAV scenarios, showing in this case that an area-segmentation approach is most effective. The results validate and provide a considerable contribution for an efficient, time-critical framework for UAV deployment in complex, real-world SAR operations.