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

arXiv: https://arxiv.org/abs/2206.10544 · HTML: https://mlsanity.com/paper/2206.10544/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 106 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [The Robotarium: A remotely accessible swarm robotics research testbed](https://mlsanity.com/paper/1609.04730/) (2016)
- [Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation](https://mlsanity.com/paper/1702.00884/) (2017)
- [Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.04244/) (2018)
- [Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming](https://mlsanity.com/paper/2201.08484/) (2022)
- [Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles](https://mlsanity.com/paper/1905.00492/) (2019)
- [Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires](https://mlsanity.com/paper/2006.07969/) (2020)
- [Heterogeneous Graph Attention Networks for Learning Diverse Communication](https://mlsanity.com/paper/2108.09568/) (2021)
- [A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking](https://mlsanity.com/paper/1803.07926/) (2018)
- [A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking](https://mlsanity.com/paper/1704.02630/) (2017)
- [Multi-Robot Control Using Time-Varying Density Functions](https://mlsanity.com/paper/1404.0338/) (2014)
- [Safe Coordination of Human-Robot Firefighting Teams](https://mlsanity.com/paper/1903.06847/) (2019)
- [An Optimal Control Approach to the Multi-Agent Persistent Monitoring Problem in Two-Dimensional Spaces](https://mlsanity.com/paper/1308.0345/) (2013)

## Built on by (most cited first, 4 of 4)

- [PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments](https://mlsanity.com/paper/2403.11095/) (2024)
- [Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation](https://mlsanity.com/paper/2406.18595/) (2024)
- [ED3R: Energy-Aware Distributed Disaster Detection via Cooperative Agents in Robotic Systems](https://mlsanity.com/paper/2606.17739/) (2026)
- [D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems](https://mlsanity.com/paper/2609.07350/) (2026)

## Abstract (arXiv)

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.