Human-Centered Development of Guide Dog Robots: Quiet and Stable Locomotion Control
Shangqun Yu, Hochul Hwang, Trung Dang, Joydeep Biswas, Nicholas Giudice, Sunghoon Ivan Lee, Donghyun Kim
AI summary
Problem
Existing quadruped guide robots produce excessive noise and jerky motions that disrupt navigation for blind and low-vision individuals, while prior research lacks a holistic, user-centric approach to quiet and stable locomotion.
Approach
Researchers interviewed BLV handlers and conducted participatory walking trials to identify key needs, then developed a real-time nonlinear model predictive control framework integrated with terrain perception to enable quiet, stable walking and stair climbing.
Key results
- Identified locomotion requirements via stakeholder interviews and blindfolded trials
- Achieved up to 10 dB noise reduction and 50% lower noise than default controllers
- Enabled perception-guided stair climbing and robust balance recovery
- Secured higher user acceptance and reduced workload in BLV trials
Why it matters
This work provides a practical, user-validated control framework that bridges robotic locomotion engineering with the real-world accessibility needs of blind and low-vision individuals.
Abstract
A quadruped robot is a promising system that can offer assistance comparable to that of guide dogs due to its similar form factor. However, various challenges remain in making these robots a reliable option for blind and low- vision (BLV) individuals. Among these challenges, noise and jerky motion during walking are critical drawbacks of exist- ing quadruped robots. While these issues have largely been overlooked in guide dog robot research, our interviews with guide dog handlers and trainers revealed that acoustic and physical disturbances can be particularly disruptive for BLV individuals, who rely heavily on environmental sounds for nav- igation. To address these issues, we developed a novel walking controller for slow stepping and smooth foot swing/contact while maintaining human walking speed, as well as robust and stable balance control. The controller integrates with a perception system to facilitate locomotion over non-flat terrains, such as stairs. Our controller was extensively tested on the Unitree Go1 robot and, when compared with other control methods, demonstrated significant noise reduction – half of the default locomotion controller. To evaluate the usability, workload, and perceived noise of the developed system from a user’s perspective, we conducted indoor walking experiments. In these tests, participants compared our controller with the robot’s default controller. The results demonstrated higher user acceptance of our controller, highlighting its potential to improve the overall user experience of robotic guide dogs. Video demonstration (best viewed with audio) available at: https: //guidedogrobot-stairclimbing.github.io.