An Optimization Model of UAV-aided Searching Procedures for Mountain Rescue

Unmanned aerial vehicles (UAVs) have been considered as cost-effective and flexible remote sensing platforms in scenarios like natural disaster monitoring, vegetation mapping and mountain rescue. In the field of mountain rescue, one of the critical problems comes from the lack of evidence or empirical data to measure or qualify the effectiveness of mountain rescue UAV-aided operations due to the number of variables.

This research aims to investigate and develop a systematic UAV-aided searching model in mountain rescue based on the existing modelling techniques, such as the Monte Carlo tree search algorithm, global sensitivity analysis, and co-simulation. Another main research focus is finding the primary factors for both detection probabilities and effective sweep width from the current state-of-art in search theories. Once the UAV-aided mountain rescue model is developed, it will be used to run a series of virtual field trials and practical mountain rescue standard operating procedures.

The potential applications of this research mainly come from two aspects. First, we will design and introduce a searching model, which summarises and lists a series of critical factors in UAV-aided mountain rescue operations. The second contribution is providing UAV operating procedures for search and rescue teams, including detailed rescue strategies, path planning trajectories, recommended object detection algorithms and other associated technical guidance. The mountain rescue standard operating procedures aim to provide specific UAV related advice and recommendation to the mountain rescue teams.