Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown characteristics, such as size, scale, and propagation velocity, and to plan accordingly. In this paper, we propose a distributed control framework to coordinate a team of unmanned aerial vehicles (UAVs) for a human-centered active sensing of wildfires. We develop a dual-criterion objective function based on Kalman uncertainty residual propagation and weighted multi-agent consensus protocol, which enables the UAVs to actively infer the wildfire dynamics and parameters, track and monitor the fire transition, and safely manage human firefighters on the ground using acquired information. We evaluate our approach relative to prior work, showing significant improvements by reducing the environment's cumulative uncertainty residual by more than $ 10^2 $ and $ 10^5 $ times in firefront coverage performance to support human-robot teaming for firefighting. We also demonstrate our method on physical robots in a mock firefighting exercise.

中文翻译：

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以人为本的野火主动感知无人机协同控制

扑灭野火是一项岌岌可危的任务，危及参与消防员和居住在火灾路径上的人们的生命。消防员需要对火线进行在线动态观察，以预测野火的未知特征，例如规模、规模和传播速度，并据此制定计划。在本文中，我们提出了一个分布式控制框架来协调无人机（UAV）团队，以实现以人为中心的野火主动感知。我们开发了一个基于卡尔曼不确定性残差传播和加权多智能体共识协议的双标准目标函数，使无人机能够主动推断野火动态和参数，跟踪和监控火灾转变，并安全地管理地面上的人类消防员使用获得的信息。我们相对于之前的工作评估了我们的方法，通过将环境的累积不确定性残差减少超过 10 ^ 2 美元和 10 ^ 5 美元的火线覆盖性能，以支持人机协作进行灭火，从而显示出显着的改进。我们还在模拟消防演习中展示了我们在物理机器人上的方法。