Browsing by Author "Chuang, Tzu-Kuan"

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Deep Trail-Following Robotic Guide Dog in Pedestrian Environments for People who are Blind and Visually Impaired - Learning from Virtual and Real Worlds
(2018-01-01)
Chuang, Tzu-Kuan
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Lin, Ni-Ching
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Chen, Jih-Shi
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Hung, Chen-Hao
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Huang, Yi-Wei
Navigation in pedestrian environments is critical to enabling independent mobility for the blind and visually impaired (BVI) in their daily lives. White canes have been commonly used to obtain contact feedback for following walls, curbs, or man-made trails, whereas guide dogs can assist in avoiding physical contact with obstacles or other pedestrians. However, the infrastructures of tactile trails or guide dogs are expensive to maintain. Inspired by the autonomous lane following of self-driving cars, we wished to combine the capabilities of existing navigation solutions for BVI users. We proposed an autonomous, trail-following robotic guide dog that would be robust to variances of background textures, illuminations, and interclass trail variations. A deep convolutional neural network (CNN) is trained from both the virtual and real-world environments. Our work included major contributions: 1) conducting experiments to verify that the performance of our models trained in virtual worlds was comparable to that of models trained in the real world; 2) conducting user studies with 10 blind users to verify that the proposed robotic guide dog could effectively assist them in reliably following man-made trails.
Duckiepond: An Open Education and Research Environment for a Fleet of Autonomous Maritime Vehicles
(2019-01-01)
Lin, Ni-Ching
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Hsiao, Yu-Chieh
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Huang, Yi-Wei
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Hung, Ching-Tung
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Chuang, Tzu-Kuan
Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duck-ieboat. Duckieboats are designed to be easily reproducible with parts from a 3D printer and other commercially available parts, with flexible software that leverages several open source packages. The Duckiepond environment is modeled after Duck-ietown and AI Driving Olympics environments: Duckieboats rely only on one monocular camera, IMU, and GPS, and perform all ML processing using onboard embedded computers. Duckiepond coordinates commonly used middlewares (ROS and MOOS) and containerized software packages in Docker, making it easy to deploy. The combination of learning-based methods together with classic methods enables important maritime missions: track and trail, navigation, and coordinate among Duck-ieboats to avoid collisions. Duckieboats have been operating in a man-made lake, reservoir and river environments. All software, hardware, and educational materials are openly available (https://robotx-nctu.github.io/duckiepond), with the goal of supporting research and education communities across related domains.