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NRI: FND: Scene Understanding and Predictive Monitoring for Safe Human-Robot Collaboration in Unstructured and Dynamic Construction Environments

NRI：FND：场景理解和预测监控，实现非结构化和动态施工环境中的安全人机协作

基本信息

批准号：
1734266
负责人：
SangHyun Lee
金额：
$ 75万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734266&HistoricalAwards=false
关键词：
NRI FND Scene Understanding Predictive

项目摘要

The construction industry has the highest number of fatalities and injuries due to hazardous working conditions. The introduction of robots on construction sites has the potential to relieve human workers from dangerous and repetitive tasks by making machines intelligent and autonomous. However, robotic solutions for construction face significant challenges. This project will develop technologies of automated monitoring and intervention through computer vision to provide a means to dramatically improve the perception of construction safety in the presence of co-robots. The new methods developed in this project will impact computer vision, machine learning, and effective human-robot collaboration in unstructured environments, while significantly contributing to safety. Further, the developed methodologies can be broadly applicable in situations where robots are deployed in human-centered environments (hospitals, airports, shipyards, etc.) and have other priorities such as productivity and efficiency as their objective. This project will engage a diverse group of individuals by training graduate and undergraduate students (including women and underrepresented minorities), reaching out to K-12 students, and interacting with industry professionals for broad dissemination of the research results.This research will investigate new computer vision based methods that can be coupled with other sensing modalities for holistic understanding and predictive analysis of jobsite safety on co-robotic construction sites. The project will consist of two main research thrusts. First, holistic scene understanding will be pursued on construction sites using graphical models to enable joint reasoning of various scene components. This holistic understanding in turn will help evaluate compliance with established safety rules expressed as formal statements. Second, predictive analysis will be investigated by exploiting the fact that, for safety intervention, the complex dynamics of a construction scene make it necessary to simulate what will happen next. In particular, Recurrent Neural Networks will be leveraged to predict future events and prevent impending accidents. Finally, an integrated demonstration system will be built and tested on real construction sites.

建筑业由于危险的工作条件而造成的伤亡人数最多。在建筑工地上引入机器人有可能通过使机器智能化和自主化，将人类工人从危险和重复性的任务中解放出来。然而，用于建筑的机器人解决方案面临着重大挑战。该项目将通过计算机视觉开发自动监控和干预技术，以提供一种方法，大大提高在协作机器人存在下的施工安全感。该项目开发的新方法将影响计算机视觉、机器学习和非结构化环境中的有效人机协作，同时为安全性做出重大贡献。此外，所开发的方法可以广泛适用于机器人部署在以人为中心的环境（医院，机场，造船厂等）中的情况。并以生产力和效率等其他优先事项为目标。这个项目将通过培训研究生和本科生来吸引不同群体的个人（包括妇女和代表性不足的少数民族），接触K-12学生，这项研究将探讨新的基于计算机视觉的方法，可以与其他传感模式相结合，用于全面理解和预测分析工地安全，机器人建筑工地该项目将包括两个主要研究方向。首先，将使用图形模型对建筑工地进行整体场景理解，以实现各种场景组件的联合推理。这种全面的理解反过来将有助于评估是否符合以正式声明形式表达的既定安全规则。其次，预测分析将通过利用这样一个事实进行研究，即，对于安全干预，施工现场的复杂动态使得有必要模拟接下来会发生什么。特别是，将利用递归神经网络来预测未来事件并防止即将发生的事故。最后，将建立一个集成的演示系统，并在真实的建筑工地上进行测试。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection

DOI：
10.22260/isarc2019/0131
发表时间：
2019-05
期刊：
Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC)
影响因子：
0
作者：
Daeho Kim;Meiyin Liu;SangHyun Lee;V. Kamat
通讯作者：
Daeho Kim;Meiyin Liu;SangHyun Lee;V. Kamat

Enhancing Deep Neural Network-Based Trajectory Prediction: Fine-Tuning and Inherent Movement-Driven Post-Processing

DOI：
10.1061/9780784482872.008
发表时间：
2020-11
期刊：
影响因子：
0
作者：
Daeho Kim;Houtan Jebelli;Sang Hyun Lee;V. Kamat
通讯作者：
Daeho Kim;Houtan Jebelli;Sang Hyun Lee;V. Kamat

Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D

DOI：
发表时间：
2020-12
期刊：
ArXiv
影响因子：
0
作者：
Ankit Goyal;Kaiyu Yang;Dawei Yang;Jia Deng
通讯作者：
Ankit Goyal;Kaiyu Yang;Dawei Yang;Jia Deng

Reality Capture Technologies (LiDAR, RGB-D, Vision)

现实捕捉技术（LiDAR、RGB-D、视觉）

DOI：
10.1061/9780784482438.019
发表时间：
2019
期刊：
Fast Dataset Collection Approach for Articulated Equipment Pose Estimation
影响因子：
0
作者：
Liang, Ci-Jyun;Lundeen, Kurt M.;McGee, Wes;Menassa, Carol C.;Lee, SangHyun;Kamat, Vineet R.
通讯作者：
Kamat, Vineet R.

PackIt: A Virtual Environment for Geometric Planning