Paper ID: 2410.08507 • Published Oct 11, 2024
Decentralized Uncertainty-Aware Active Search with a Team of Aerial Robots
Wennie Tabib, John Stecklein, Caleb McDowell, Kshitij Goel, Felix Jonathan, Abhishek Rathod, Meghan Kokoski, Edsel Burkholder...
TL;DR
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Rapid search and rescue is critical to maximizing survival rates following
natural disasters. However, these efforts are challenged by the need to search
large disaster zones, lack of reliability in the communications infrastructure,
and a priori unknown numbers of objects of interest (OOIs), such as injured
survivors. Aerial robots are increasingly being deployed for search and rescue
due to their high mobility, but there remains a gap in deploying multi-robot
autonomous aerial systems for methodical search of large environments. Prior
works have relied on preprogrammed paths from human operators or are evaluated
only in simulation. We bridge these gaps in the state of the art by developing
and demonstrating a decentralized active search system, which biases its
trajectories to take additional views of uncertain OOIs. The methodology
leverages stochasticity for rapid coverage in communication denied scenarios.
When communications are available, robots share poses, goals, and OOI
information to accelerate the rate of search. Extensive simulations and
hardware experiments in Bloomingdale, OH, are conducted to validate the
approach. The results demonstrate the active search approach outperforms greedy
coverage-based planning in communication-denied scenarios while maintaining
comparable performance in communication-enabled scenarios.