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

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引信的智能无人机自动探测埋在地下的爆炸物来帮助人道主义、科学和国防努力。埋藏爆炸物探测技术的不一致使补救工作进展缓慢，并阻碍了受雷区影响最严重的国家的农业发展。拟议的技术提供了一个机会，通过在单一平台上使用多个传感器并减少进行自动航空测量所需的操作员数量，来降低探测埋藏爆炸物的成本。该项目的教育影响包括对该项目的博士生进行创业培训，以及通过该项目的技术原型的实践演示向未被充分代表的社区进行当地科学推广，以激发人们对地球物理、机器学习和工程学的兴趣。该项目将通过提供一种快速评估哪套仪器将产生最高掩埋目标识别精度的方法，来进一步加深对近地表(0-2米)的地球物理理解。无人驾驶飞机将使用机器学习产生野外区域的复合反演。通过机器学习将多台地球物理仪器的数据进行最佳组合，可以增加推断的置信度。来自该项目的见解可能有助于改进基于地球科学中多个互补的地球物理数据集的推断。在该项目的研究和开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。该项目将有助于以一种实时和环境适应的方式弥合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，特别是在地面野外工作成本高昂或危险的环境中。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.