NeTS: Small: NSF-DST: Modernizing Underground Mining Operations with Millimeter-Wave Imaging and Networking

该项目旨在通过采用毫米波（mmWave）无线技术（5G及超越标准的核心技术），解决地下采矿环境中传感和网络的独特挑战。这项技术特别适用于恶劣条件的地下矿山，如灰尘和低光或黑暗的条件。然而，由于在复杂结构中重建高质量的3D地图，融合静态和移动地下实时地图以及部署毫米波通信基础设施，在采矿中采用毫米波技术具有挑战性。通过克服这些挑战，该项目旨在提高采矿作业的安全性，通过更好的资源管理、导航和机械定位提高作业效率，并通过推进自主采矿系统的未来为国家利益做出贡献。与以采矿专业知识而闻名的印度机构的合作，由印度科技部（DST）资助，旨在推进现代采矿技术的最新水平。该项目通过为学生提供实践经验，将研究成果整合到研究生课程中，以及展示原型来支持教育和多样性。这个项目的技术贡献包括三个主要方面。Thrust 1强调使用毫米波设备进行稳健的3D制图，通过动态速度估计和深度学习解决运动误差和信号稀疏等挑战，同时集成移动和静态地图以实现全面的环境结构。Thrust 2旨在与毫米波传感一起建立弹性网络，以实现快速连接和精确测绘，解决通信系统信号和延迟挑战，并利用深度学习在矿山中高效部署毫米波。Thrust 3提出了一个全面的评估计划，通过实验室规模的原型测试和采矿模拟器的试验来验证早期的推力。此外，该项目在真实采矿环境中的毫米波测试平台上设计并经验验证了所提出的系统。该项目符合全球可持续发展目标，并解决了现代采矿业面临的一些紧迫挑战。该项目由网络技术和系统（NeTS）计划和促进竞争研究的既定计划（EPSCoR）共同资助。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。

The project aims to address the unique challenges of sensing and networking in underground mining environments by employing millimeter-wave (mmWave) wireless, a core technology for 5G and beyond standards. This technology is particularly suited for the harsh conditions of underground mines, such as dust and low light or dark conditions. However, the adoption of mmWave technology in mining is challenging due to reconstructing high-quality 3D maps in complex structures, fusing static and mobile underground real-time maps, and deploying mmWave communication infrastructures. By overcoming these challenges, this project seeks to enhance safety in mining operations, improve operational efficiency through better resource management, navigation, and machinery positioning, and contribute to the national interest by advancing the future of autonomous mining systems. The collaboration with Indian institutions, known for their mining expertise, funded by the Department of Science and Technology (DST), India, aims to advance the state of the art in modern mining technology. This project supports education and diversity by providing hands-on experiences to students, integrating research results into graduate-level courses, and demonstrating prototypes. The technical contributions of this project involve three main thrusts. Thrust 1 emphasizes robust 3D mapping with mmWave devices, addressing challenges like motion error and signal sparsity through dynamic velocity estimation and deep learning while integrating mobile and static maps for a comprehensive environment structure. Thrust 2 aims to establish resilient networking alongside mmWave sensing for rapid connectivity and precise mapping, addressing communication systems signaling and latency challenges and leveraging deep learning for efficient mmWave deployment in mines. Thrust 3 presents a comprehensive evaluation plan for validating earlier thrusts through tests with lab-scale prototypes and trials in a mining simulator. Furthermore, the project designs and empirically validates the proposed systems in a mmWave testbed inside a real-world mining environment. This project aligns with global sustainability goals and addresses some of the pressing challenges in the modern mining industry.This project is jointly funded by the Networking Technology and Systems (NeTS) program, and the Established Program to Stimulate Competitive Research (EPSCoR).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.