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

通过传感器的最佳定位在商业环境中安全地进行机器人导航和基础设施数据采集

• 批准号: 515725-2017
• 负责人: RamirezSerrano, Alejandro
• 金额: $ 1.82万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640671
• 关键词: Safe; robot; navigation; infrastructure; data

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.

资本项目聘请工程师根据项目计划设计资产。零件在世界各地的工厂制造并运送到在许多情况下不适合现有基础设施的现场。这些因素和其他因素每年给资本项目行业造成 1.6 万亿美元的损失。使用革命性的、全面的端到端软件解决方案来解决工业返工问题，并促进主动、具有成本效益的项目执行通过收集各种感官和空间数据生成的数字项目信息，以创建物理项目的数字孪生。被使用。然而，通常手动执行的数据收集需要实现自动化，以实现有效的资本项目执行，同时保持所需的传感器数据准确性。通过识别现实与计划之间的不匹配，自动传感器数据收集将在项目执行的早期阶段采取纠正措施，避免导致返工。该项目的重点是通过使用自主无人地面车辆（UGV）实现资本项目快速数据收集的自动化。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和操纵 UGV 以最佳方式定位传感器，以收集相关且准确的数据。视觉输入（LIDAR 传感器）将用作反馈，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功能。重点是开发工具，用于 (1) 准确检测商业场所中常见的复杂障碍物（例如，高架电缆），(2) 通过视觉伺服/测量进行 UGV 路径规划，以实现最佳传感器放置，以及 (3) 分析使用的有效性建筑工地中的 UGV。