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工作之一，与此类操作相关的事故经常导致建筑工地和周边地区的死亡和财产损失。此类事故还可能造成相当大的延误和干扰，并对业务效率产生不利影响。该奖项将通过整合机器人、计算机视觉和施工管理方面的进步，开展研究，以提高起重机的安全性和效率。它将创建用于快速简便地规划起重机操作的工具，并将其纳入一个安全有效的系统，该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。安全性和效率的提高将减少致命和非致命起重机事故。与工业界的伙伴关系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，包括K-12学生。这项工作的假设是，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，详细和连续的监测和控制反馈可以改善设备操作的规划和模拟。它将特别侧重于开发以下方法：（a）规划施工作业，同时通过模拟对安全隐患进行核算;（B）对设备状态进行估计和分析;（c）监测起重机作业环境周围的设备，包括检测安全隐患，以及对包括材料、设备和工人在内的动态资源进行邻近分析;（d）实时控制起重机的稳定性;以及（e）使用视觉和触觉提示在“透明驾驶舱”中向用户和设备操作员提供反馈。它将通过故障影响分析和创建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三维重建技术，上下文驱动的物体识别和预测物体的运动轨迹来改善监测;通过动态起重机模型、不稳定性测量和用于寻找最佳控制的算法来增强控制的可靠性;以及最后，通过用于提供视觉和触觉提示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.