A platform for high accuracy real-time tracking of dynamic multi-objects using auto-positioning and artificial intelligence to increase safety in construction sites

利用自动定位和人工智能对动态多对象进行高精度实时跟踪的平台，以提高建筑工地的安全性

• 批准号: 571594-2021
• 负责人: Sadeghpour, Farnaz
• 金额: $ 3.64万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=729188
• 关键词: platform; accuracy; real; time; tracking

Construction has one of the highest rates of injuries and fatalities among all industries in Canada. Real-time locating systems (RTLS) have proven to be able to alleviate safety issues by increasing situational awareness of workers. Ultrawide Band (UWB) tracking systems have shown a high potential for precise tracking of dynamic objects in indoor environments due to their high accuracy, high signal penetration properties, and low power consumption. However, due to the requirement for onerous measurements at installation, the need for cable connectivity, and high cost, their widespread use for precision tracking in workplace environments has been a long-standing challenge. The objective of the proposed study is to develop an accurate, lithe, inexpensive, and easy-to-deploy platform for precise real time tracking of moving objects in indoor environments. The platform will use inexpensive, off-the-shelf two-way ranging UWB radios which drastically reduce the cost of tracking. The two-way ranging eliminates the need for cable connectivity. Auto-positioning algorithms are used to eliminate the need for onerous calibration at installation. A newly developed filtering algorithm will be used to enhance the accuracy of auto-positioning to the similar or better accuracy that is achieved with calibration. Machine learning and artificial intelligence techniques will be used to overcome the impact of time latency error that is important in tracking moving objects. The functionality and accuracy of the proposed location estimation platform will be measured in the lab environment, and it will be iteratively calibrated to achieve high accuracy. While the immediate end-user for the platform is the construction industry, there is a strong potential and demand for precise real-time tracking of moving multi-objects in other industries where real-time situational awareness of dynamic resources (e.g. people or equipment) can enhance safety, security and productivity; e.g. tracking valuable moving resources in healthcare facilities, tracking patients in senior citizen facilities, or tracking equipment on manufacturing floors. The platform can offer a competitive edge to various Canadian industries including construction, manufacturing, healthcare, and robotics.

建筑业是加拿大所有行业中受伤和死亡率最高的行业之一。实时定位系统(RTL)已被证明能够通过提高工人的情景意识来缓解安全问题。超宽带(UWB)跟踪系统由于其高精度、高信号穿透性和低功耗等特点，在室内环境中对动态目标的精确跟踪具有很高的潜力。然而，由于安装时需要繁琐的测量、需要电缆连接以及高昂的成本，它们在工作场所环境中的精确跟踪的广泛使用一直是一个长期的挑战。这项研究的目标是开发一个准确、柔软、廉价、易于部署的平台，用于精确实时跟踪室内环境中的运动目标。该平台将使用价格低廉、现成的UWB双向测距无线电，从而大大降低跟踪成本。双向测距消除了对电缆连接的需要。自动定位算法用于消除安装时繁琐的校准需要。将使用一种新开发的过滤算法来提高自动定位的精度，使其达到与校准相似或更好的精度。将使用机器学习和人工智能技术来克服时间延迟误差的影响，时间延迟误差在跟踪运动对象时非常重要。拟议的位置估计平台的功能和精度将在实验室环境中进行测量，并将进行迭代校准，以实现高精度。虽然该平台的直接最终用户是建筑行业，但在其他行业中，对动态资源(例如人员或设备)的实时情景感知可以提高安全性、安全性和工作效率，例如跟踪医疗设施中的宝贵移动资源、跟踪老年人设施中的患者或制造楼层中的跟踪设备，因此对移动多目标的精确实时跟踪具有很强的潜力和需求。该平台可以为包括建筑、制造、医疗保健和机器人在内的加拿大各种行业提供竞争优势。