@techreport{ZellSchneidereitFuegenschuhetal.2024, type = {Working Paper}, author = {Zell, Sascha and Schneidereit, Toni and F{\"u}genschuh, Armin and Breuß, Michael}, title = {Advanced search and rescue operations for drowning swimmers using autonomous unmanned aircraft systems : location optimization, flight trajectory planning and image-based localization}, doi = {10.26127/BTUOpen-6866}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-68669}, year = {2024}, abstract = {Drowning is among the most prevalent causes of death from unintentional injuries worldwide. Because of the time-sensitive nature of swimming accidents and the shortage of lifeguard staff resulting in unsupervised swimming areas, interest in supportive rescue methods increases. In this paper, we propose an autonomous Unmanned Aircraft System (UAS) usable by Emergency Medical Service (EMS) providers in swimmer rescue scenarios additionally to Standard Rescue Operation (SRO) equipment. The UAS consists of Unmanned Aerial Vehicles (UAVs) and purpose-built hangars located near the swimming area to store the UAVs. When receiving an alert, the UAVs autonomously navigate to the emergency site to conduct a Search and Rescue (S\&R) operation for the drowning person. We introduce a Mixed-Integer Linear Programming (MILP) model to address the Facility Location Problem (FLP), assisting with identification of accessibility-optimal UAV hangar placements near the swimming area. Additionally, we present a MILP model to optimize the UAV flight trajectories in advance of the operation, allowing for efficient coordination of a heterogeneous UAV fleet. We apply the presented MILP models to a real-world scenario in the Lusatian Lake District using the state-of-the-art commercial solver CPLEX to solve the instances. Furthermore, we present a method for automated image-based swimmer localization using the state-of-the-art neural network You Only Look Once (YOLO). Finally, we use a Discrete-Event Simulation (DES) approach to quantify how much time is saved by using additional resources.}, subject = {Unmanned Aerial Vehicle; Unmanned Aircraft System; Water Rescue; Mixed-Integer Linear Programming; You Only Look Once; Drohne; Drohnenhangar; Wasserrettung; Gemischt-ganzzahlige Programmierung; Standortoptimierung; Drohne ; Wasserrettung; Lineare Optimierung}, language = {en} } @techreport{ZellFuegenschuh2025, type = {Working Paper}, author = {Zell, Sascha and F{\"u}genschuh, Armin}, title = {Optimizing autonomous unmanned aircraft system deployment locations for enhanced wildfire detection and monitoring}, doi = {10.26127/BTUOpen-6942}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-69422}, year = {2025}, abstract = {The increase in the frequency and severity of wildfires as a symptom of climate change requires innovative methods of wildfire fighting. For this reason, we propose a framework for an autonomous Unmanned Aircraft System (UAS), consisting of a fleet of Micro Air Vehicles (MAVs) stored in purpose-built hangars. The intention is to deploy the UAS as the first responder to an alarm and have the sensor-equipped MAVs monitor the target area even before other standard firefighting vehicles have arrived. The focus of this paper is primarily on the development and application of a location-allocation optimization Mixed-Integer Linear Programming (MILP) model that selects different MAV and hangar types and locates them, with the objective of approaching the target area as quickly as possible while guaranteeing a certain monitoring time at the scene. The model is applied to a large, sparsely populated, rural operational area, around a third of which consists of forest in the South of Brandenburg, Germany. The spatial demand is measured through an easily reproducible and transferable open data approach. Finally, several instances with different fixed numbers of hangars and MAVs to be set up are solved by the commercial state-of-the-art solver CPLEX and analyzed for their computation time.}, subject = {Mixed-lnteger linear programming; Unmanned aircraft systems; Micro air vehicles; Autonomous wildfire monitoring; Location-allocation optimization; Gemischt-ganzzahlige Programmierung; Drohnen; Drohnenhangar; Standortproblem; Autonome Waldbrandbek{\"a}mpfung; Waldbrand; {\"U}berwachung; Drohne (Flugk{\"o}rper); Autonomes System; Standortproblem; Lineare Optimierung}, language = {en} }