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Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

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Co-Robotic Cane: A New Robotic Navigation Aid for the Visually Impaired.

Cang Ye¹, Soonhac Hong², Xiangfei Qian²

¹Dept. of Systems Engineering, University of Arkansas at Little Rock, Little Rock, Arkansas 72204 United States.

IEEE Systems, Man, and Cybernetics Magazine

|November 1, 2019

Summary

This study introduces the Co-Robotic Cane (CRC), a novel navigation aid using a 3D camera for indoor environments. The CRC enhances mobility by offering both active robotic guidance and passive computer-vision-assisted navigation.

Keywords:

3D object recognition co-robot egomotion estimation human-robot interaction pose estimation pose graph optimization robotic navigation aid

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Area of Science:

Robotics
Computer Vision
Human-Computer Interaction

Background:

Traditional mobility aids lack advanced environmental perception.
Need for intelligent navigation systems to assist visually impaired individuals or in complex indoor settings.

Purpose of the Study:

To develop and present the Co-Robotic Cane (CRC), an intelligent navigation aid.
To enable autonomous pose estimation and object recognition for enhanced indoor navigation.
To provide adaptive assistance through dual active and passive modes.

Main Methods:

Utilizes a 3D camera for egomotion estimation and Iterative Closest Point (ICP) algorithm for 6-DOF pose estimation.
Employs pose graph optimization to reduce accumulated pose errors.
Implements a Gaussian Mixture Model (GMM) for recognizing indoor structures (stairways, doorways) and objects (tables, monitors).

Main Results:

Achieved accurate pose estimation without prior environmental knowledge.
Successfully identified key indoor structures for navigation waypoints and obstacle avoidance.
Demonstrated dual-mode functionality (active robot cane and passive computer-vision-enhanced white cane).

Conclusions:

The Co-Robotic Cane (CRC) offers a versatile and intelligent solution for indoor navigation.
CRC's ability to detect human intent allows for automatic mode selection, enhancing user experience.
This co-robotic system has the potential to significantly improve user safety and independence in unfamiliar environments.