论文标题
对无人机以人为本的野火有效感应的无人机控制
Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires
论文作者
论文摘要
战斗野火是一项不稳定的任务，这损害了吸引消防员和居住在火道路的人的生活。消防人员需要在线和动态观察火front，以预测野火的未知特征，例如大小，规模和传播速度，并相应地计划。在本文中，我们提出了一个分布式控制框架，以协调一个无人驾驶汽车（UAV）团队，以对以人为中心的野火进行主动感应。我们基于Kalman不确定性残差传播和加权多代理共识协议开发双重标准目标函数，该协议使无人机能够积极推断野火动态和参数，跟踪和监视火灾转变，并使用获得的信息在地面上安全地管理人类消防员。我们相对于先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留降低超过$ 10^2 $和$ 10^5 $倍的火灾覆盖效果，以支持人类机器人的消防队伍。我们还在模拟消防练习中演示了有关物理机器人的方法。
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.
