BRITE Pivot: Towards Intelligent Health Monitoring, Inspection, and Reconnaissance of Critical Infrastructure using Autonomous Robots

这一促进工程转型和公平进步的研究想法(BRITE)项目将通过在机器学习、优化和计算机视觉领域的新研究，提高自主机器在最少人工协助下检查关键基础设施的能力。这一目标将通过一个由课堂学习和重点研究任务组成的有指导和严格的计划来实现，该计划旨在提高小型无人机(无人机)远程自主检查新的、未知的和杂乱的基础设施环境的能力，以利用计算机视觉发现潜在威胁。通过在机器学习、优化和计算机视觉的尖端理论和方法方面教育和培训一批土木工程研究人员，该项目将使未来能够在与自治基础设施和智能城市相关的一系列令人兴奋和具有社会变革意义的主题上进行研究。如果没有自主的检查、监控和侦察解决方案，工程师就会受到环路中人类的瓶颈制约。这项研究通过探索空中机器人自主导航、建模和检查杂乱的城市环境的方法来解决这一关键差距。计算机视觉、机器学习和人工智能将用于改善在自然和/或人为危害之前和之后对基础设施的侦察。机器自主和智能地为我们社会的安全和功能做出贡献的潜力不仅将改善监测社会基础设施的经济性，而且还将通过帮助维持一个更具功能性和安全性的社会来改善其居民的生活质量。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。

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) project will advance the ability of autonomous machines to inspect critical infrastructure with minimal human assistance through novel research in areas of machine learning, optimization, and computer vision. This objective will be achieved through a mentored and rigorous program comprised of classroom learning and focused research tasks intended to advance the ability for a small unmanned aerial vehicle (UAV or “drone”) to remotely and autonomously inspect a new, unknown, and cluttered infrastructure environment to find a potential threat using computer vision. By educating and training a group of civil engineering researchers in cutting-edge theory and methods of machine learning, optimization, and computer vision, this project will enable future research in a wide range of exciting and societally transformative topics related to autonomous infrastructure and smart cities. Without autonomous inspection, monitoring, and reconnaissance solutions, engineers are bottlenecked by humans in the loop. This research addresses the critical gap by exploring means for aerial robots to autonomously navigate in, model, and inspect cluttered urban environments. Computer vision, machine learning, and artificial intelligence will be used to improve reconnaissance of infrastructure before and after natural and/or man-made hazards. The potential for machines to autonomously and intelligently contribute to the safety and functionality of our society will not only improve the economics of monitoring society’s infrastructure, but it will also improve the quality of life for its residents by helping to maintain a more functional and safe society.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.