CPS/Synergy/Collaborative Research: Safe and Efficient Cyber-Physical Operation System for Construction Equipment

设备操作是建筑工地上最危险的任务之一，与这种操作有关的事故经常造成建筑工地和周围地区的死亡和财产损失。此类事故还会造成相当大的延误和中断，并对运营效率产生负面影响。该合同将通过整合机器人技术、计算机视觉和施工管理方面的先进技术来开展研究，以提高起重机的安全性和效率。它将为起重机操作的快速和简单规划创造工具，并将其纳入一个安全有效的系统，可以监控起重机的环境，并向起重机和操作员提供控制反馈。由此带来的安全和效率的提高将减少致命和非致命的起重机事故。与行业的合作也将确保这些进步对建筑实践产生积极影响，并可广泛扩展到智能基础设施、智能制造、监控、交通监控和其他应用领域。这项研究将涉及本科生，并将扩展到K-12学生。这项工作是基于这样一个假设，即起重机的监测和控制可以使用机器人技术和计算机视觉算法自主执行，而详细和持续的监测和控制反馈可以改进设备操作的规划和模拟。它将特别侧重于制定以下方法：(a)规划施工作业，同时通过模拟计算安全隐患；(b)对设备状态进行估计和分析；(c)监控起重机运行环境周围的设备，包括安全隐患的检测，以及对动态资源（包括材料、设备和工人）的接近性分析；(d)实时控制起重机稳定性；(e)在“透明驾驶舱”中使用视觉和触觉线索向用户和设备操作员提供反馈。它将通过失效效应分析和创造接触状态估计和平衡分析方法来提高规划的效率和可靠性，从而解决潜在的研究挑战；通过模型驱动和实时3D重建技术、上下文驱动的对象识别和预测对象的运动轨迹来改进监测；通过动态起重机模型、不稳定性措施和寻找最优控制的算法来提高控制的可靠性；最后，通过提供视觉和触觉线索的方法来提高反馈回路的效率。

Equipment operation represents one of the most dangerous tasks on a construction sites and accidents related to such operation often result in death and property damage on the construction site and the surrounding area. Such accidents can also cause considerable delays and disruption, and negatively impact the efficiency of operations. This award will conduct research to improve the safety and efficiency of cranes by integrating advances in robotics, computer vision, and construction management. It will create tools for quick and easy planning of crane operations and incorporate them into a safe and efficient system that can monitor a crane's environment and provide control feedback to the crane and the operator. Resulting gains in safety and efficiency wil reduce fatal and non-fatal crane accidents. Partnerships with industry will also ensure that these advances have a positive impact on construction practice, and can be extended broadly to smart infrastructure, intelligent manufacturing, surveillance, traffic monitoring, and other application areas. The research will involve undergraduates and includes outreach to K-12 students.The work is driven by the hypothesis that the monitoring and control of cranes can be performed autonomously using robotics and computer vision algorithms, and that detailed and continuous monitoring and control feedback can lead to improved planning and simulation of equipment operations. It will particularly focus on developing methods for (a) planning construction operations while accounting for safety hazards through simulation; (b) estimating and providing analytics on the state of the equipment; (c) monitoring equipment surrounding the crane operating environment, including detection of safety hazards, and proximity analysis to dynamic resources including materials, equipment, and workers; (d) controlling crane stability in real-time; and (e) providing feedback to the user and equipment operators in a "transparent cockpit" using visual and haptic cues. It will address the underlying research challenges by improving the efficiency and reliability of planning through failure effects analysis and creating methods for contact state estimation and equilibrium analysis; improving monitoring through model-driven and real-time 3D reconstruction techniques, context-driven object recognition, and forecasting motion trajectories of objects; enhancing reliability of control through dynamic crane models, measures of instability, and algorithms for finding optimal controls; and, finally, improving efficiency of feedback loops through methods for providing visual and haptic cues.