Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees

TL;DR

This paper introduces a predictive multi-UAV framework for wildfire coverage and tracking that guarantees performance over time by inferring fire dynamics, outperforming existing methods in accuracy and resource distribution.

Contribution

It presents a novel probabilistic approach enabling UAV teams to reason about fire propagation and provide performance guarantees, applicable to various safety-critical multi-robot tasks.

Findings

Achieved 7.5x smaller tracking error than model-based benchmarks.

Achieved 9.0x smaller tracking error than reinforcement learning benchmarks.

Validated effectiveness through simulation and physical robot experiments.

Abstract

In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been commissioned by researchers to enable accurate, online wildfire coverage and tracking. While the majority of prior work focuses on the coordination and control of such multi-robot systems, to date, these UAV teams have not been given the ability to reason about a fire's track (i.e., location and propagation dynamics) to provide performance guarantee over a time horizon. Motivated by the problem of aerial wildfire monitoring, we propose a predictive framework which enables cooperation in multi-UAV teams towards collaborative field coverage and fire tracking with probabilistic performance guarantee. Our approach enables UAVs to infer the latent fire propagation dynamics for time-extended coordination in safety-critical conditions. We derive a set of novel, analytical temporal, and tracking-error bounds to enable the UAV-team to distribute their limited resources and cover the entire fire area according to the case-specific estimated states and provide a probabilistic performance guarantee. Our results are not limited to the aerial wildfire monitoring case-study and are generally applicable to problems, such as search-and-rescue, target tracking and border patrol. We evaluate our approach in simulation and provide demonstrations of the proposed framework on a physical multi-robot testbed to account for real robot dynamics and restrictions. Our quantitative evaluations validate the performance of our method accumulating 7.5x and 9.0x smaller tracking-error than state-of-the-art model-based and reinforcement learning benchmarks, respectively.