ERI: Wind Field Estimation and Path Planning for Uncrewed Aerial Vehicles in Urban Environments

ERI：城市环境中无人驾驶飞行器的风场估计和路径规划

• 批准号: 2301475
• 负责人: Artur Wolek
• 金额: $ 19.99万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301475&HistoricalAwards=false
• 关键词: ERI; Wind; Field; Estimation; Path

The use of uncrewed aerial vehicles in urban centers will benefit society and national security through applications such as commercial/medical package delivery, public safety, military surveillance, and infrastructure inspection. However, operating small, lightweight, aerial vehicles at building-level altitudes remains a challenge, especially in the presence of strong winds that exacerbate the risk of collision with people or property. Complex wind patterns around buildings and other structures can result in sudden unanticipated disturbances, increased power consumption, and degraded vehicle performance and stability. This Engineering Research Initiation (ERI) award aims to improve the fundamental understanding of how aerial robots with limited computational capabilities can collaboratively estimate and exploit a complex urban wind field to plan safer and more efficient flight paths. New algorithms will be designed and analyzed that combine information relating the layout of buildings with physics-based simulations of wind flows to create a realistic and computationally efficient wind field estimation algorithm. A path planning technique will also be established that uses the wind field estimate to more accurately predict vehicle motion to improve safety. The knowledge generated by this project can be adopted to enable future aerial vehicles to operate in urban wind fields that are prohibitive for existing systems. Additionally, this project will include outreach activities to inspire interest in STEM among middle-school-age children and will recruit undergraduate students in the research program with focus on underrepresented groups.The specific objective of this research is to establish and analyze novel algorithms that enable multiple aerial robots to collaboratively build spatial maps of complex urban wind fields and exploit them for path planning at building-level altitudes. First, a modern data-driven estimation approach, such as Gaussian process regression, will assimilate local along-path wind measurements to predict the global wind field and its associated spatial uncertainty. Each measurement will consist of a spatial position, wind velocity, and an environment feature vector that characterizes local building morphology. Hyper-parameters of the estimator will be trained utilizing computational fluid dynamics simulations of urban wind fields to produce a physics-informed wind estimator that is cognizant of the environment geometry. Second, a chance-constrained path planning algorithm will be established to minimize energy usage subject to collision-risk constraints that are quantified using the predicted wind-field, its uncertainty, and the closed-loop path following vehicle dynamics. The wind mapping accuracy, computational efficiency, and reduction in collision risk of the planned approach will be assessed through numerical simulations and small-scale outdoor flight experiments with anemometer-equipped quadrotors.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

在城市中心使用无人驾驶飞行器，将通过商业/医疗包裹递送、公共安全、军事监视和基础设施检查等应用，造福社会和国家安全。然而，在建筑高度操作小型轻型飞行器仍然是一个挑战，特别是在强风加剧与人员或财产碰撞风险的情况下。建筑物和其他结构周围复杂的风模式会导致突然的意外干扰，增加电力消耗，降低车辆性能和稳定性。该工程研究启动（ERI）奖旨在提高对具有有限计算能力的空中机器人如何协同估计和利用复杂的城市风场来规划更安全和更有效的飞行路径的基本理解。将设计和分析新的算法，将建筑物布局相关信息与基于物理的风流模拟相结合，以创建一个现实且计算效率高的风场估计算法。此外，还将建立一种路径规划技术，利用风场估计更准确地预测车辆运动，以提高安全性。这个项目产生的知识可以被用来使未来的飞行器在城市风场中运行，这是现有系统所禁止的。此外，该项目将包括外展活动，以激发中学生对STEM的兴趣，并将招募本科生参与研究项目，重点关注代表性不足的群体。本研究的具体目标是建立和分析新的算法，使多个空中机器人能够协同构建复杂城市风场的空间地图，并利用它们进行建筑高度的路径规划。首先，现代数据驱动估计方法，如高斯过程回归，将吸收局部沿程风测量来预测全球风场及其相关的空间不确定性。每个测量将包括空间位置、风速和表征当地建筑形态的环境特征向量。利用城市风场的计算流体动力学模拟来训练估计器的超参数，以产生一个能够识别环境几何形状的物理信息风估计器。其次，将建立一个机会约束路径规划算法，在碰撞风险约束下最小化能源消耗，碰撞风险约束使用预测风场、风场不确定性和车辆动态闭环路径进行量化。将通过配备风速计的四旋翼机进行数值模拟和小规模室外飞行实验，评估计划方法的风测绘精度、计算效率和碰撞风险降低。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。