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.

Public Health Relevance

The co-robotic navigation aid and the related technology address a growing public health care issue -- visual impairment and target improving the life quality of the blind. The research fits well into the Rehabilitation Engineering Program ofthe NIBIB that includes robotics rehabilitation. The proposed research addresses to the NlBlB's mission through developing new computer vision and HDl technologies for blind navigation.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Special Emphasis Panel ()
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Peng, Grace
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University of Arkansas at Little Rock
Engineering (All Types)
Schools of Engineering
Little Rock
United States
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Ye, Cang; Qian, Xiangfei (2018) 3-D Object Recognition of a Robotic Navigation Aid for the Visually Impaired. IEEE Trans Neural Syst Rehabil Eng 26:441-450
Zhang, He; Ye, Cang (2017) An Indoor Wayfinding System Based on Geometric Features Aided Graph SLAM for the Visually Impaired. IEEE Trans Neural Syst Rehabil Eng 25:1592-1604
Ye, Cang; Hong, Soonhac; Tamjidi, Amirhossein (2015) 6-DOF Pose Estimation of a Robotic Navigation Aid by Tracking Visual and Geometric Features. IEEE Trans Autom Sci Eng 12:1169-1180
Qian, Xiangfei; Ye, Cang (2014) NCC-RANSAC: a fast plane extraction method for 3-D range data segmentation. IEEE Trans Cybern 44:2771-83