将智能感知与导航融入商用电动轮椅,实现远程召唤和自动跟行。
Towards Real-World Applications with an Autonomous Powered Wheelchair

- 融合RGB-D相机与激光雷达,实现环境感知与自主导航。
- 支持远距离召唤和室内跟随功能,验证系统可行性。
- 适合残障人士辅助出行研究者及智能轮椅开发者参考。
轮椅用户亟需能主动支持、适应动态环境且操作直观的辅助移动系统。然而,现有电动轮椅普遍自主性有限,缺乏先进感知与导航能力的集成,尤其在复杂真实环境中表现不足。本文提出一种面向真实应用的自主电动轮椅初步研究,构建了一个概念验证原型,集成自主感知、基于手势的交互与导航功能于一款商用自平衡电动轮椅Genny Zero上。该轮椅通过身体重心移动实现无手操作。为拓展其自主能力,系统引入了用于人机感知的RGB-D相机与用于定位导航的LiDAR传感器。实验展示了两种辅助应用场景:(i) 远程召唤,用户可在远处呼叫轮椅;(ii) 人员跟随,采用领导者-跟随者策略实现自动追踪,包括受限室内导航示例。结果表明,该集成方案具备实现智能辅助移动的潜力,同时揭示了迈向用户可用、可访问、智能化移动解决方案前仍需克服的主要技术挑战。演示视频见:https://youtu.be/LVAix_Qx7bM。
原文摘要 · Abstract (English)
Wheelchair users call for assistive mobility systems that provide active support, adapt to dynamic environments, and are intuitive and user-friendly. However, powered wheelchairs typically still provide limited autonomy and lack effective integration with advanced perception and navigation capabilities, particularly in complex real-world environments. This paper presents a preliminary study toward autonomous powered wheelchairs for real-world assistive mobility. We introduce a proof-of-concept prototype that integrates autonomous perception, gesture-based interaction, and navigation on a commercially available self-balancing powered wheelchair. The proposed system builds upon Genny Zero, a commercial self-balancing wheelchair that enables hands-free and intuitive operation through body-weight shifting. To extend its capabilities toward autonomous operation, we integrate an RGB-D camera for human-aware perception and interaction, together with a LiDAR sensor for localization and navigation. We demonstrate the integrated system in two assistive applications: (i) hailing, allowing users to call the wheelchair from a distance; and (ii) people-following, where the wheelchair follows a person using leader-follower strategies, including a constrained indoor navigation example. The results highlight the potential of combining autonomous robotics with assistive mobility platforms, while also showing the feasibility of the proposed integration and identifying the main technical challenges that must be addressed before moving toward user-ready, accessible, and intelligent mobility solutions. A video demonstrating the experimental setup and results is available at: https://youtu.be/LVAix_Qx7bM.
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