_:n62a0251cce17434fab2843caf5fa4858xb51
<http://schema.org/description>
"In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been\ncommissioned by researchers to enable accurate, online wildfire coverage and\ntracking. While the majority of prior work focuses on the coordination and\ncontrol of such multi-robot systems, to date, these UAV teams have not been\ngiven the ability to reason about a fire's track (i.e., location and\npropagation dynamics) to provide performance guarantee over a time horizon.\nMotivated by the problem of aerial wildfire monitoring, we propose a predictive\nframework which enables cooperation in multi-UAV teams towards collaborative\nfield coverage and fire tracking with probabilistic performance guarantee. Our\napproach enables UAVs to infer the latent fire propagation dynamics for\ntime-extended coordination in safety-critical conditions. We derive a set of\nnovel, analytical temporal, and tracking-error bounds to enable the UAV-team to\ndistribute their limited resources and cover the entire fire area according to\nthe case-specific estimated states and provide a probabilistic performance\nguarantee. Our results are not limited to the aerial wildfire monitoring\ncase-study and are generally applicable to problems, such as search-and-rescue,\ntarget tracking and border patrol. We evaluate our approach in simulation and\nprovide demonstrations of the proposed framework on a physical multi-robot\ntestbed to account for real robot dynamics and restrictions. Our quantitative\nevaluations validate the performance of our method accumulating 7.5x and 9.0x\nsmaller tracking-error than state-of-the-art model-based and reinforcement\nlearning benchmarks, respectively."
<https://deepai.org/profile/esmaeil-seraj>