PFI-RP: Automatic detection of buried explosives using a multi-sensor smart aerial vehicle

PFI-RP：使用多传感器智能飞行器自动检测埋藏爆炸物

• 批准号: 2044611
• 负责人: Daniel Lathrop
• 金额: $ 54.98万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044611&HistoricalAwards=false
• 关键词: PFI; RP; Automatic; detection; buried

The broader impact/commercial potential of this Partnerships for Innovation - Research Partnerships (PFI-RP) project aims to assist in humanitarian, scientific, and defense endeavors by automatically detecting buried explosives using a sensor-fused smart unmanned aircraft. Inconsistent buried explosives detection techniques have made remediation slow and stymied agricultural development in countries most impacted by minefields. The proposed technology offers an opportunity to decrease the cost of buried explosives detection by employing multiple sensors on a single platform and reducing the number of operators needed to conduct the automated aerial survey. Educational impacts of this project include the entrepreneurial training of the project’s PhD student, and local scientific outreach to underrepresented communities through hands-on demonstrations of the project’s technology prototype to inspire interest in geophysics, machine learning, and engineering.This project will further the geophysical understanding of the near-surface (0-2 meters) by providing a method for quickly evaluating which set of instruments will yield the highest buried target identification accuracy. The unmanned aircraft will produce a composite inversion of a field area using machine learning. Optimally combining data from several geophysical instruments through machine learning can increase the confidence of inferences. Insights from this project may help improve inferences based on multiple, complementary geophysical datasets across the geosciences. Using multiple machine learning algorithms in the research and development portion of this project will help inform future applications. This project will help bridge the gap of geophysical data collection and inversions in a real-time and environment-adaptable way. Beyond the scope of buried explosives detection, this project’s technology platform and deep learning package will enable end-users to better differentiate what geologic and man-made materials are present in the subsurface using geophysical data, especially in environments where ground-based fieldwork is costly or hazardous.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.

这个创新研究伙伴关系（PFI-RP）项目的更广泛的影响/商业潜力旨在通过使用传感器融合的智能无人驾驶飞机自动检测埋在地下的爆炸物来协助人道主义，科学和国防工作。埋置爆炸物探测技术的不一致，使受雷场影响最严重的国家的补救工作进展缓慢，阻碍了农业发展。拟议的技术提供了一个机会，通过在一个平台上使用多个传感器和减少进行自动空中调查所需的操作人员的数量，降低了埋藏爆炸物探测的成本。该项目的教育影响包括项目博士生的创业培训，以及通过项目技术原型的实际演示，激发对物理学、机器学习、该项目将进一步加深对近地表的地球物理认识，（0-2米），提供了一种快速评估哪套仪器将产生最高的埋地目标识别精度的方法。 无人驾驶飞机将使用机器学习产生场区域的复合反演。通过机器学习将来自多个地球物理仪器的数据进行最佳组合可以提高推断的置信度。 该项目的见解可能有助于改进基于地球科学领域多个互补地球物理数据集的推断。在该项目的研发部分使用多种机器学习算法将有助于为未来的应用提供信息。该项目将有助于以实时和适应环境的方式弥补地球物理数据收集和反演的差距。除了地下爆炸物探测的范围外，该项目的技术平台和深度学习包将使最终用户能够使用地球物理数据更好地区分地下存在的地质和人造材料，尤其是在地面-基于现场的研究是昂贵的或危险的。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值的支持和更广泛的影响审查标准。