Anticipatory Human-Robot Path Planning for Search and Rescue
This addresses the challenge of improving search efficiency in rescue operations for teams of human searchers and UAVs, though it appears incremental by extending existing paradigms with specific modeling techniques.
The paper tackles the problem of coordinating autonomous UAVs with human searchers in search and rescue missions by developing a framework that uses simulated behavior models and Gaussian processes to create probabilistic heatmaps and optimize UAV paths, resulting in a system that autonomously complements human efforts.
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.