Enabling Shared-Control for A Riding Ballbot System

This study introduces a shared-control approach for collision avoidance in the self-balancing riding ballbot, PURE, marked by its dynamic stability, omnidirectional movement, and hands-free interface. Integrating a sensor array with a novel Passive Artificial Potential Field (PAPF) method, PURE provides intuitive navigation with deceleration assistance and haptic/audio feedback, effectively mitigating collision risks. This approach addresses the limitations of traditional APF methods, such as control oscillations and unnecessary speed reduction in challenging scenarios. A human subject test, with 20 manual wheelchair users and able-bodied individuals, was conducted to evaluate the performance of indoor navigation and obstacle avoidance with the proposed shared-control algorithm. Results showed that shared-control significantly reduced collisions and cognitive load without affecting travel speed, offering intuitive and safe operation. These findings highlight the shared-control system’s suitability for enhancing collision avoidance in self-balancing mobility devices, a relatively unexplored area in assistive mobility research.

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Publication Details

Published
2025-08-17
DOI
https://doi.org/10.1109/case58245.2025.11164039
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
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article

Enabling Shared-Control for A Riding Ballbot System

Mahshid Mansouri, Yu Chen, Chenzhang Xiao, William R. Norris et al.
Robotic Path Planning Algorithms
article

Enabling Shared-Control for A Riding Ballbot System

Mahshid Mansouri, Yu Chen, Chenzhang Xiao, William R. Norris, Ze Wang, Elizabeth T. Hsiao‐Wecksler
article en

Abstract

This study introduces a shared-control approach for collision avoidance in the self-balancing riding ballbot, PURE, marked by its dynamic stability, omnidirectional movement, and hands-free interface. Integrating a sensor array with a novel Passive Artificial Potential Field (PAPF) method, PURE provides intuitive navigation with deceleration assistance and haptic/audio feedback, effectively mitigating collision risks. This approach addresses the limitations of traditional APF methods, such as control oscillations and unnecessary speed reduction in challenging scenarios. A human subject test, with 20 manual wheelchair users and able-bodied individuals, was conducted to evaluate the performance of indoor navigation and obstacle avoidance with the proposed shared-control algorithm. Results showed that shared-control significantly reduced collisions and cognitive load without affecting travel speed, offering intuitive and safe operation. These findings highlight the shared-control system’s suitability for enhancing collision avoidance in self-balancing mobility devices, a relatively unexplored area in assistive mobility research.

University of Illinois Urbana-Champaign (US)
Openalex Percentile: Top 74%
Robotic Path Planning Algorithms
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