2020/09/08 by Barnabas Gavin Cangan, Larkin Heintzman, Cangan, Barnabas Gavin +7
Computer Science · Engineering · #60G15 #FOS: Computer and information sciences #I.2.9 #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization #UAV Applications and Optimization
paper · pdf · doi:10.48550/arxiv.2009.03976
openalex publication_date 2020/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, our goal is to extend the existing search and rescue paradigm by allowing teams of autonomous unmanned aerial vehicles (UAVs) to collaborate effectively with human searchers on the ground. We derive a framework that includes a simulated lost person behavior model, as well as a human searcher behavior model that is informed by data collected from past search tasks. These models are used together to create a probabilistic heatmap of the lost person's position and anticipated searcher trajectories. We then use Gaussian processes with a Gibbs' kernel to accurately model a limited field-of-view (FOV) sensor, e.g., thermal cameras, from which we derive a risk metric that drives UAV path optimization. Our framework finally computes a set of search paths for a team of UAVs to autonomously complement human searchers' efforts.