NRI: INT: Safe Wind-Aware Navigation for Collaborative Autonomous Aircraft in Low Altitude Airspace

NRI：INT：低空空域协作自主飞机的安全风感知导航

• 批准号: 1925147
• 负责人: He Bai
• 金额: $ 149万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925147&HistoricalAwards=false
• 关键词: NRI; INT; Safe; Wind; Aware

This project will promote the progress of science, and advance the national health, security and prosperity by conducting fundamental research needed to enhance safety and efficiency of aircraft operations in low altitude urban environments. Safe and seamless inclusion of unmanned aircraft such as delivery drones, and manned aircraft such as air taxis into urban airspace requires building traffic management systems that account for the performance of aircraft, pilots, and autopilots under a variety of atmospheric conditions. Aircraft operating in low altitude urban environments are subject to turbulent wind gusts that cannot be accurately predicted using current techniques. This research project will address this challenge by enabling the progress of science across multiple disciplines, including meteorology, human-robot interaction, machine learning, data-driven modeling, robotics, and instrumentation. Desired research outcomes will significantly impact many important applications, including micro-weather prediction, turbulent plumes of pollutants and emissions, drone package delivery, and air taxis. The inter-disciplinary nature of the project will better prepare next-generation students and engineers and the synergistic activities will broaden the participation of underrepresented groups in research.The project aims to validate the hypothesis that 'in-time' gust awareness by a pilot or an autopilot, can enhance safety, efficiency and robustness of future autonomous aircraft operations in low altitude airspace. Towards this objective, the research team will investigate novel learning tools to model piloting behaviors, design safe and efficient wind aware path planning algorithms, and importantly, construct short-term gust forecast models with wind measurements. The team will develop a high-fidelity simulation framework that integrates turbulence modeling, guidance, navigation, control, and pilot-aircraft interface to demonstrate autonomous and remotely-piloted aircraft flying through urban canopies with improved predictability and increased endurance. A recommendation system that facilitates pilot-aircraft interactions will be produced and demonstrated through both simulations and experiments.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.

该项目将通过开展提高低空城市环境中飞机运行安全性和效率所需的基础研究，促进科学进步，促进国家健康，安全和繁荣。要将无人驾驶飞机（如送货无人机）和有人驾驶飞机（如空中出租车）安全无缝地纳入城市空域，需要建立交通管理系统，以考虑各种大气条件下飞机、飞行员和自动驾驶仪的性能。在低空城市环境中运行的飞机会受到湍流阵风的影响，而使用现有技术无法准确预测。 该研究项目将通过推动多个学科的科学进步来应对这一挑战，包括气象学、人机交互、机器学习、数据驱动建模、机器人技术和仪器仪表。期望的研究成果将对许多重要应用产生重大影响，包括微天气预测、污染物和排放物的湍流羽流、无人机包裹递送和空中出租车。该项目的跨学科性质将更好地培养下一代学生和工程师，协同活动将扩大代表性不足的群体在研究中的参与。该项目旨在验证飞行员或自动驾驶仪“及时”阵风感知的假设，可以提高未来自主飞机在低空空域操作的安全性，效率和鲁棒性。为了实现这一目标，研究小组将研究新的学习工具来模拟驾驶行为，设计安全有效的风感知路径规划算法，重要的是，构建具有风测量的短期阵风预测模型。该团队将开发一个高保真仿真框架，集成湍流建模，制导，导航，控制和驾驶员-飞机接口，以演示自主和远程驾驶飞机在城市树冠中飞行，提高可预测性和耐力。该奖项反映了NSF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。

项目成果

Wind Speed Statistics from a Small UAS and Its Sensitivity to Sensor Location

小型无人机的风速统计及其对传感器位置的敏感性

• DOI: 10.3390/atmos13030443
• 发表时间: 2022
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Wilson, Trevor C.;Brenner, James;Morrison, Zachary;Jacob, Jamey D.;Elbing, Brian R.
• 通讯作者: Elbing, Brian R.

A non-intrusive reduced order model using deep learning for realistic wind data generation for small unmanned aerial systems in urban spaces

• DOI: 10.1063/5.0098835
• 发表时间: 2022-08
• 期刊: AIP Advances
• 影响因子: 1.6
• 作者: Rohit K. S. S. Vuppala-Rohit-K.-S.-S.-Vuppala-151196900;Kursat Kara

Switched Optimal Control and Dwell Time Constraints: A Preliminary Study

切换最优控制和停留时间约束：初步研究

• DOI: 10.1109/cdc42340.2020.9304087
• 发表时间: 2020
• 期刊: Proceedings of the IEEE Conference on Decision and Control
• 作者: Abudia, Moad;Harlan, Michael;Self, Ryan;Kamalapurkar, Rushikesh
• 通讯作者: Kamalapurkar, Rushikesh

Parameterized input inference for approximate stochastic optimal control

近似随机最优控制的参数化输入推理

• DOI: 10.23919/acc55779.2023.10155809
• 发表时间: 2023
• 期刊: Proceedings of the American Control Conference
• 作者: Syed, Shahbaz P;Bai, He
• 通讯作者: Bai, He

Preliminary Design of Wind-Aware sUAS Simulation Pipeline for Urban Air Mobility

城市空中交通风感知小型无人机仿真流程的初步设计

• DOI: 10.2514/6.2022-3872
• 发表时间: 2022
• 期刊: AIAA Aviation 2022 Forum
• 作者: Tabassum, Asma;DeSantis, Max;Bai, He;Fala, Nicoletta
• 通讯作者: Fala, Nicoletta