Terrain-Aware Probabilistic Search Planning for Unmanned Aerial Vehicles (I)

Abstract

Mountain search and rescue is a form of emergency response to assist people in austere environments (e.g., extreme terrain, poor weather). Volunteer mountain search and rescue teams in the United States have begun adopting consumer-grade unmanned aerial vehicles to assist a variety of tasks (e.g., search, resource delivery); however, these tools lack the autonomy necessary for the mountain search and rescue teams to fully realize their potential for wide area, aerial search. The unique and tight constraints of mountain search and rescue (e.g., in situ computation, sensor limitations) greatly limit the applicability of recent robotics research. A two-step coverage path planning algorithm that leverages existing viewpoint and path planning approaches was developed to meet the unique needs of mountain search and rescue. Viewpoints were sampled to meet a minimum coverage ratio and assigned priority from a search probability map. The path planning problem was formulated as a clustered traveling salesperson problem, which is solved with a metaheuristic iterative solver. Simulation results inform parameter selection for a series of field experiments. The field experiments demonstrate how the new algorithm can provide resilience against the many compounding factors that make UAV-based mountain search and rescue challenging