NRI: Small: A Co-Robotic Navigation Aid for the Visually Impaired

描述(由申请者提供)：该项目的目标是为视障人士开发一种名为协同机器人手杖(CRC)的协同机器人导航辅助设备。CRC能够通过直观的人-设备交互(HDL)机制与其用户协作，以在3D环境中进行有效导航。CRCS的导航功能包括设备位置估计、路线查找、障碍物检测/避让和目标识别。儿童康复中心的使用将改善视障人士的独立行动能力，从而提高他们的生活质量。该提案的教育计划是让研究生、本科生和高中生参与该项目，并利用该项目的活动来招收和留住工程专业的学生。该提案的智力价值在于开发了新的计算机视觉方法，支持3D环境中准确的盲目导航和直观的高密度脂蛋白接口，以有效使用设备。这些方法包括：(1)一种新的机器人位姿估计方法，通过集成运动估计和视觉特征跟踪来提供准确的设备位姿；(2)基于高斯混合模型的模式识别方法，可以识别室内结构和物体以进行路线识别和障碍物操纵/回避；(3)创新的机制，用于直观地传达所需的行进方向；以及(4)用于自动设备模式切换的人类意图检测界面。计算机视觉方法的GPU实现将使实时执行成为可能。拟议的盲目导航解决方案将赋予CRC先进的导航功能，而这些功能是现有的盲人导航辅助设备目前无法提供的。PI的团队已经对计算机视觉方法进行了概念验证研究，结果令人振奋。更广泛的影响包括：(1)这些方法的短期应用将影响大型视障社区；(2)这些方法将提高小型机器人和便携式机器人设备的自主性，这些设备在军事监视、执法和搜救中有无数应用；以及(3)该项目将改善PI大学的研究基础设施，并为他们未来的科学和工程职业生涯培养研究生和本科生。

DESCRIPTION (provided by applicant): The project's objective is to develop enabling technology for a co-robotic navigation aid, called a Co-Robotic Cane (CRC), for the visually impaired. The CRC is able to collaborate with its user via intuitive Human-Device Interaction (HDl) mechanisms for effective navigation in 3D environments. The CRCs navigational functions include device position estimation, wayfinding, obstacle detection/avoidance, and object recognition. The use of the CRC will improve the visually impaired's independent mobility and thus their quality of life. The proposal's educational plan is to involve graduate, undergraduate and high school students in the project, and use the project's activities to recruit and retain engineering students. The proposal's Intellectual Merit is the development of new computer vision methods that support accurate blind navigation in 3D environments and intuitive HDl interfaces for effective use of device. These methods include: (1) a new robotic pose estimation method that provides accurate device pose by integrating egomotion estimation and visual feature tracking; (2) a pattern recognition method based on the Gaussian Mixture Model that may recognize indoor structures and objects for wayfinding and obstacle manipulation/ avoidance; (3) an innovative mechanism for intuitive conveying of the desired travel direction; and (4) a human intent detection interface for automatic device mode switching. The GPU implementation of the computer vision methods will make real-time execution possible. The proposed blind navigation solution will endow the CRC with advanced navigational functions that are currently unavailable in the existing blind navigation aids. The PI's team has performed proof of concept studies for the computer vision methods and the results are promising. The broader impacts include: (1) the methods' near term applications will impact the large visually impaired community; (2) the methods will improve the autonomy of small robots and portable robotic devices that have a myriad of applications in military surveillance, law enforcement, and search and rescue; and (3) the project will improve the research infrastructure of the Pi's university and train graduate and undergraduate students for their future careers in science and engineering.