实现骑乘式球机器人系统的共享控制
Enabling Shared-Control for A Riding Ballbot System
AI总结：
本研究针对自平衡骑乘式球机器人PURE，提出基于新型被动人工势场的共享控制避障方法，经20名受试者实验验证，可在不影响速度的前提下减少碰撞、降低认知负荷，填补了辅助移动领域相关研究空白。
摘要：
本研究提出了一种用于自平衡骑乘式球机器人PURE避障的共享控制方法，该机器人以动态稳定性、全向移动和免手持界面为特点。PURE集成了传感器阵列和新型被动人工势场（Passive Artificial Potential Field, PAPF）方法，可提供直观导航、减速辅助以及触觉/音频反馈，有效降低碰撞风险。该方法解决了传统人工势场（APF）方法在复杂场景中存在的控制振荡、不必要减速等局限性。研究开展了一项人机交互实验，招募了20名手动轮椅使用者和健全人，评估所提共享控制算法的室内导航与避障性能。结果表明，共享控制在不影响行进速度的前提下显著减少了碰撞次数、降低了认知负荷，实现了直观且安全的操作。这些发现凸显了共享控制系统适用于提升自平衡移动设备的避障能力，而这一领域在辅助移动研究中尚未得到充分探索。
英文摘要：
This study introduces a shared-control approach for collision avoidance in a self-balancing riding ballbot, called PURE, marked by its dynamic stability, omnidirectional movement, and hands-free interface. Integrated with a sensor array and 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-robot interaction experiment, 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 indicated 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
