Specialized localization interface technologies for industrial robotic inspection

用于工业机器人检测的专业定位接口技术

• 批准号: 499663-2016
• 负责人: Young, James
• 金额: $ 5.89万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 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=618848
• 关键词: Specialized; localization; interface; technologies; industrial

Industrial inspection robot use is booming, e.g., in oil and gas wells, for inspections from a safe, remote location. Although robot technology is maturing, human-machine interfaces are limited, often having only raw video feeds and simple joysticks to remotely control a robot. Improved interface technologies are needed to support operator awareness of remote robots within environments, to improve operation performance. Through this project we will invent and prototype next-generation robotic interfaces, providing operators with intuitive access to cutting edge robot localization technologies and algorithms. While robot localization - the knowledge of where a robot is within an environment - is an established research area, there is little work on how this technology can be used by non-expert robot operators. E.g., maps can be difficult to interpret, crucial details for real-world use is often omitted (e.g., uncertainty, error) and operators cannot provide input at run time (e.g., to mark an error). Further, industrial partner Inuktun Services Ltd. has indicated that existing packages do not work with their environments, e.g., robots in pipe networks or attached to boat hulls, where global features, common edges such as walls, corners, and doors, are missing. Our team expertise includes human-robot interaction and interface design (Dr. Young), human perception (Dr. Bruce), deep learning (Dr. Wang), and computer vision (Drs. Bruce, Wang) expertise, which will provide HQP with multi-disciplinary training in employable areas including robotics, computer vision, and interface design. We will prototype and evaluate novel localization interfaces and algorithms for practical industrial inspection robots. We will research interfaces to help operators interpret output from, and provide input to, localization software, and novel algorithms that provide sufficient uncertainty data, receive operator input, and work in our inspection scenario. As the Canadian robotics industry continues to emerge on the international scale, this project positions Inuktun Services Ltd. to be a leader in establishing Canada as an important player in next-generation robotic control interfaces.

工业检测机器人的使用正在蓬勃发展，例如，在油气井威尔斯中，用于从安全的远程位置进行检查。虽然机器人技术正在成熟，但人机界面有限，通常只有原始视频和简单的按钮来远程控制机器人。需要改进的接口技术来支持操作员对环境中的远程机器人的感知，以提高操作性能。通过这个项目，我们将发明和原型下一代机器人接口，为操作员提供直观的访问尖端机器人定位技术和算法。虽然机器人定位-机器人在环境中的位置的知识-是一个既定的研究领域，但关于非专家机器人操作员如何使用这项技术的工作很少。例如，在一个示例中，地图可能难以解释，用于现实世界使用的关键细节常常被省略（例如，不确定性、误差）并且操作员不能在运行时提供输入（例如，标记错误）。此外，工业合作伙伴Inuktun Services Ltd.表示，现有的软件包不适用于其环境，例如，管网中的机器人或附着在船体上的机器人，其中缺少全局特征、公共边（如墙、角和门）。我们的团队专业知识包括人机交互和界面设计（Young博士），人类感知（布鲁斯博士），深度学习（王博士）和计算机视觉（布鲁斯，王博士）专业知识，这将为HQP提供多学科培训，包括机器人，计算机视觉和界面设计。我们将为实际的工业检测机器人原型设计和评估新的定位接口和算法。我们将研究接口，以帮助操作员解释输出，并提供输入，本地化软件和新的算法，提供足够的不确定性数据，接收操作员输入，并在我们的检查方案工作。随着加拿大机器人产业在国际范围内不断崛起，该项目将使Inuktun Services Ltd.成为将加拿大打造为下一代机器人控制界面重要参与者的领导者。