Safe robot navigation and infrastructure data capture within commercial environments via optimal positioning of sensors

资本项目雇用工程师根据项目计划设计资产。零件是在世界各地的工厂制造的，然后运到现场，在许多情况下，这些地方不适合现有的基础设施。这些因素和其他因素使资本项目行业每年损失1.6万亿美元。使用一个革命性的和全面的端到端的软件解决方案，以解决工业返工的问题，并促进积极的，具有成本效益的项目执行数字项目信息通过收集各种感官和空间数据，以创建一个数字孪生的物理项目目前正在使用。然而，通常手动执行的数据收集需要自动化，以实现有效的资本项目执行，同时确保所需的传感器数据准确性。通过识别现实和计划之间的不匹配，自动化传感器数据收集将在项目执行的早期，在它们导致返工之前，使纠正措施成为可能。该项目的重点是通过使用自动化传感器，以高清晰度自动化资本项目快速数据收集无人驾驶地面车辆（UGV）。特别是，我们将开发增强型机器人的避障机制，以更好地估计传感器的位置，并操纵UGV以最佳地定位传感器，从而实现相关和准确的自动收集。视觉输入（激光雷达传感器）将被用作反馈，以伺服UGV进行3D运动，同时具有快速识别精确传感器位置的能力。重点是开发工具，用于（1）准确检测通常在商业场所发现的复杂障碍物（例如，架空电缆），（2）通过视觉伺服/测量优化传感器放置的UGV路径规划，以及（3）分析UGV在建筑工地使用的有效性。

Capital projects employ engineers to design assets based on project plans. Parts are built in factories all overthe world and shipped to site which in numerous occasions do not fit to the existing infrastructure. These andother factors cost the capital project industry $1.6 Trillion annually. Using a revolutionary and comprehensiveend-to-end software solution to solve the problem of industrial rework and facilitate proactive, cost effectiveproject execution digital project information generated via the collection of a variety of sensory and spatial datato create a Digital Twin of the physical project is currently being used. However, data collection, typicallyperformed manually, requires to be automated to enable effective capital project execution while maintainingthe required sensor data accuracy.By identifying mismatches between reality and plan, the automated sensor data collection will enable correctiveactions early in the project execution before they cause rework.The focus of this project is to automate capital project fast data collection in high definition via the use ofautonomous Unmanned Ground Vehicles (UGVs).In particular we will develop enhanced robot's obstacle avoidance mechanisms to better estimate sensorplacement and maneuvering the UGV to optimally position the sensors for relevant and accurate datacollection. A vision input (LIDAR sensor) will be used as a feedback to servo the UGV for 3D motionpurposes while having the capabilities to quickly identify precise sensor placement. The focus is on developingtools for (1) accurate detection of complex obstacles typically found in commercial sites (e.g., overhandcables), (2) UGV path planning for optimal sensor placement via visual servoing/surveying, and (3) analyze theeffectiveness of the use of UGVs in construction sites.