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

这个促进工程变革和公平进步的研究理念（BRITE）项目将通过机器学习、优化和计算机视觉领域的新研究，提高自主机器在最少的人工协助下检查关键基础设施的能力。这一目标将通过指导和严格的计划来实现，包括课堂学习和重点研究任务，旨在提高小型无人机（UAV或“无人机”）远程自主检查新的、未知的、混乱的基础设施环境的能力，利用计算机视觉发现潜在的威胁。通过在机器学习、优化和计算机视觉的前沿理论和方法方面教育和培训一批土木工程研究人员，该项目将使未来研究与自主基础设施和智慧城市相关的一系列令人兴奋和社会变革的主题成为可能。如果没有自主的检查、监控和侦察解决方案，工程师就会受到人类的瓶颈。本研究通过探索空中机器人在混乱的城市环境中自主导航、建模和检查的方法，解决了这一关键差距。计算机视觉、机器学习和人工智能将用于改善自然和/或人为灾害前后的基础设施侦察。机器自主智能地为我们社会的安全和功能做出贡献的潜力，不仅会提高监控社会基础设施的经济效益，还会通过帮助维持一个功能更强大、更安全的社会，提高居民的生活质量。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。

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.