Safe Coordination of Human-Robot Firefighting Teams
This addresses the safety and efficiency challenges for firefighters in wildfire scenarios, representing a novel method for a known bottleneck in human-robot collaboration.
The paper tackles the problem of coordinating human-robot teams in wildfire fighting by developing a model-predictive distributed control algorithm that estimates fire propagation dynamics, resulting in a probabilistic safety guarantee for humans and full fire coverage by UAVs.
Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources. Researchers have proposed commissioning unmanned aerial vehicles (UAVs) to provide firefighters with real-time tracking information; yet, these UAVs are not able to reason about a fire's track, including current location, measurement, and uncertainty, as well as propagation. We propose a model-predictive, probabilistically safe distributed control algorithm for human-robot collaboration in wildfire fighting. The proposed algorithm overcomes the limitations of prior work by explicitly estimating the latent fire propagation dynamics to enable intelligent, time-extended coordination of the UAVs in support of on-the-ground human firefighters. We derive a novel, analytical bound that enables UAVs to distribute their resources and provides a probabilistic guarantee of the humans' safety while preserving the UAVs' ability to cover an entire fire.