NRI: INT: COLLAB: Raining Drones: Mid-Air Release & Recovery of Atmospheric Sensing Systems

NRI：INT：协作：无人机下雨：空中发布

基本信息

批准号：
1924777
负责人：
Sebastian Elbaum
金额：
$ 40.35万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924777&HistoricalAwards=false
关键词：
NRI INT COLLAB Raining Drones

项目摘要

Understanding and forecasting weather is critical to nearly every industry and is even more important as weather patterns continue to change from their historical norms. Unmanned Aerial Systems (UASs) are capable of collecting valuable data in the lower kilometer of the atmosphere, which is under-sampled by traditional atmospheric sensing systems. However, a single UAS can only collect information from a small slice of the atmosphere at a time. This project will develop the systems and techniques to enable a UASs to carry, deploy, and recover smaller UASs and parachute-based weather sensors to create snapshots across atmospheric airmass boundaries. This will lead to the characterization of a much larger region of the lower atmosphere, consequently improving the understanding and forecasting of the weather. Carrying, deploying, and particularly recovering the systems, are critical to allow operations in remote locations and reuse of expensive sensors. Achieving these objectives requires new techniques to enable rich mid-air interactions between aerial robots. These techniques span from sensing and planning to control and run-time verification, all working together for the effective, efficient, and safe deployment of such teams of aerial robots. While this work is developed in the context of atmospheric sciences, the techniques are broadly applicable to many problems in distributed and collaborative robotics.This project will contribute techniques and systems to enable the development and deployment of teams of UASs that perform mid-air capture and release of other systems. More specifically, the expected outcomes of this work include: 1) Sensing, planning and control methodologies for UASs intercepting airborne targets that have stochastic full six degree of freedom motion, 2) Foundational elements for matched maneuvers between heterogeneous classes of aerial robots to perform aerial docking, 3) Strategies for rapid aerial deployment-capture-redeployment cycles for teams of UASs to enable observations over large geographic scales, 4) Run-time inference and enforcement of protocols orchestrating the interactions between distributed, heterogeneous robotic systems, and 5) Improved atmospheric sensing and monitoring capabilities that will enhance understanding and forecasting of atmospheric phenomena.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.

理解和预测天气对几乎每个行业至关重要，而且随着天气模式不断从历史规范变化而变得更加重要。无人空中系统（UASS）能够在大气的较低公里内收集有价值的数据，这是传统大气传感系统所采样不足的。但是，单个UAS只能一次从一小片大气中收集信息。该项目将开发系统和技术，以使UASS能够携带，部署和恢复较小的UASS和基于降落伞的天气传感器，以在大气空气界界创建快照。这将导致较大大气区域的更大区域的特征，从而改善对天气的理解和预测。携带，部署，尤其是恢复系统，对于允许在偏远位置进行操作以及重复使用昂贵的传感器至关重要。实现这些目标需要新技术，以使空中机器人之间有丰富的空气相互作用。这些技术从感应和计划到控制和运行时验证，都共同努力，以有效，高效且安全地部署此类航空机器人团队。尽管这项工作是在大气科学的背景下开发的，但这些技术广泛地适用于分布式和协作机器人技术的许多问题。该项目将贡献技术和系统，以使UASS团队的开发和部署在空中捕获和释放其他系统中。更具体地说，这项工作的预期结果包括：1）感应，计划和控制方法，用于UASS拦截空中目标，具有随机的全六个自由运动，2）匹配的基础元素，用于匹配的机动匹配的基础元素地理量表，4）协议的运行时间推理和执行，这些协议编排了分布式，异构机器人系统之间的相互作用，以及5）改善了大气感应和监测能力，以增强对大气现象的理解和预测，对NSF的法定任务和通过评估的支持，该奖项反映了对CR的支持，并且已被评估的知识范围和众所周知。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PhysCov: Physical Test Coverage for Autonomous Vehicles

PhysCov：自动驾驶汽车的物理测试覆盖率

DOI：
10.1145/3597926.3598069
发表时间：
2023
期刊：
Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis
影响因子：
0
作者：
Hildebrandt, Carl;von Stein, Meriel;Elbaum, Sebastian
通讯作者：
Elbaum, Sebastian

DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles

DOI：
10.1109/tse.2023.3301443
发表时间：
2023-10
期刊：
IEEE Transactions on Software Engineering
影响因子：
7.4
作者：
Meriel von Stein;David Shriver;Sebastian G. Elbaum
通讯作者：
Meriel von Stein;David Shriver;Sebastian G. Elbaum

Preparing Software Engineers to Develop Robot Systems

培养软件工程师开发机器人系统

DOI：
10.1109/icse-seet55299.2022.9794165
发表时间：
2022
期刊：
IEEE/ACM 44th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET
影响因子：
0
作者：
C. Hildebrandt, M. v.
通讯作者：
C. Hildebrandt, M. v.

Semantic image fuzzing of AI perception systems

AI感知系统的语义图像模糊

DOI：
10.1145/3510003.3510212
发表时间：
2022
期刊：
ICSE '22: Proceedings of the 44th International Conference on Software Engineering
影响因子：
0
作者：
Woodlief, Trey;Elbaum, Sebastian;Sullivan, Kevin
通讯作者：
Sullivan, Kevin

World-in-the-Loop Simulation for Autonomous Systems Validation