CPS: Small: NSF-DST: Autonomous Operations of Multi-UAV Uncrewed Aerial Systems using Onboard Sensing to Monitor and Track Natural Disaster Events

CPS：小型：NSF-DST：使用机载传感监测和跟踪自然灾害事件的多无人机无人航空系统自主操作

• 批准号: 2343062
• 负责人: Amit Sanyal
• 金额: $ 45.34万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2343062&HistoricalAwards=false
• 关键词: CPS; Small; NSF; DST; Autonomous

This research project focuses on using uncrewed aerial systems (UAS) to monitor and track natural disaster events like wildland fires and flooding rivers and lakes. As the intensities of these events continue to increase worldwide due to climate change, the need for their early monitoring and tracking has also increased. A UAS, consisting of a team of uncrewed aerial vehicles (UAVs) and one or more ground stations, can provide real-time monitoring and tracking of unfolding disaster events, as well as help with relief operations to avoid large scale losses of lives and property. This project brings together US and Indian experts working on autonomous UAS operations in the presence of environmental uncertainties and hazards. In particular, it seeks to understand how teams of autonomous UAVs could be used to maximize the data gathered and predict the intensity and spread of forest fires and floods. Through its outreach activities, this project team will also encourage students to participate in research on autonomous vehicles and inform the general public about the value of such research in addressing societal challenges. Key research goals are to design and use nonlinearly stable and robust motion estimation and control schemes that enable a multi-UAV team to collaboratively follow desired trajectories for loitering, monitoring and tracking in the presence of disturbances like wind and air currents. Each UAV in the UAS is modeled as an actuated rigid body, making the UAS a multi-agent rigid body system (MARBS). Geometric controller and observer designs will be developed that are computationally light and can be implemented with commercially available sensors onboard rotorcraft UAVs. Sensor data from inertial sensors and point cloud sensors like depth cameras, will be combined using a continuous and finite-time stable extended state observer (ESO). This ESO will provide estimates of UAV motion states, relative pose of other UAVs within the range of point cloud sensors, and disturbance estimates. These estimates will be used by feedback tracking controllers that are designed to track desired trajectories in a stable manner while rejecting the disturbances, thereby providing active disturbance rejection control (ADRC). Both asymptotically stable and finite-time stable control laws will be designed, and the entire navigation and control system for a multi-UAV team will be tested in indoor and outdoor environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目的重点是使用无人驾驶航空系统（UAS）来监测和跟踪自然灾害事件，例如荒地火灾和河流和湖泊洪水。由于气候变化，这些事件的强度在全球范围内持续增加，对其早期监测和跟踪的需求也随之增加。无人机系统由一组无人驾驶飞行器（UAV）和一个或多个地面站组成，可以实时监测和跟踪正在发生的灾害事件，并帮助开展救援行动，避免大规模生命和财产损失。该项目汇集了美国和印度专家，致力于在环境不确定性和危险存在的情况下进行无人机自主操作。特别是，它试图了解如何使用自主无人机团队来最大化收集的数据并预测森林火灾和洪水的强度和蔓延。通过其外展活动，该项目团队还将鼓励学生参与自动驾驶汽车的研究，并向公众宣传此类研究在应对社会挑战方面的价值。主要研究目标是设计和使用非线性稳定且鲁棒的运动估计和控制方案，使多无人机团队能够在存在风和气流等干扰的情况下协作遵循所需的轨迹进行徘徊、监视和跟踪。 UAS 中的每个无人机都被建模为驱动刚体，使 UAS 成为多智能体刚体系统 (MARBS)。将开发计算量小的几何控制器和观察器设计，并且可以使用旋翼无人机上的市售传感器来实现。来自惯性传感器和点云传感器（例如深度相机）的传感器数据将使用连续且有限时间稳定的扩展状态观察器（ESO）进行组合。该 ESO 将提供无人机运动状态的估计、点云传感器范围内其他无人机的相对姿态以及干扰估计。这些估计将由反馈跟踪控制器使用，反馈跟踪控制器旨在以稳定的方式跟踪所需轨迹，同时抑制干扰，从而提供主动干扰抑制控制（ADRC）。将设计渐近稳定和有限时间稳定控制律，并在室内和室外环境中测试多无人机团队的整个导航和控制系统。该奖项反映了 NSF 的法定使命，并被认为值得支持使用基金会的智力价值和更广泛的影响审查标准进行评估。