用AI帮普通人快速诊断ROS机器人错误,减少停机时间。
ROS Help Desk: GenAI Powered, User-Centric Framework for ROS Error Diagnosis and Debugging
- 基于用户水平自适应的智能诊断框架,支持多模态数据融合
- 能主动发现错误,实测可显著缩短维护时间
- 适合缺乏ROS经验的普通用户和工业运维人员
随着机器人系统日益融入日常生活,从智能家居助手到新一代工业自动化(工业4.0),亟需弥合复杂机器人系统与普通用户之间的鸿沟。机器人操作系统(ROS)虽灵活易用,但其分布式架构和复杂通信机制使非专业用户难以理解系统状态和排查错误,导致维护耗时长、故障频发。ROS Help Desk 提供面向用户的直观错误解释与调试支持,动态适配不同知识水平的使用者。系统具备用户中心化调试工具、主动错误检测能力,并融合多传感器数据(如激光雷达、RGB)实现全系统状态感知。通过人工引入错误的定性与定量测试,验证了其在主动、精准诊断问题上的有效性,显著降低维护时间,促进人机协作效率提升。
原文摘要 · Abstract (English)
As the robotics systems increasingly integrate into daily life, from smart home assistants to the new-wave of industrial automation systems (Industry 4.0), there's an increasing need to bridge the gap between complex robotic systems and everyday users. The Robot Operating System (ROS) is a flexible framework often utilised in writing robot software, providing tools and libraries for building complex robotic systems. However, ROS's distributed architecture and technical messaging system create barriers for understanding robot status and diagnosing errors. This gap can lead to extended maintenance downtimes, as users with limited ROS knowledge may struggle to quickly diagnose and resolve system issues. Moreover, this deficit in expertise often delays proactive maintenance and troubleshooting, further increasing the frequency and duration of system interruptions. ROS Help Desk provides intuitive error explanations and debugging support, dynamically customized to users of varying expertise levels. It features user-centric debugging tools that simplify error diagnosis, implements proactive error detection capabilities to reduce downtime, and integrates multimodal data processing for comprehensive system state understanding across multi-sensor data (e.g., lidar, RGB). Testing qualitatively and quantitatively with artificially induced errors demonstrates the system's ability to proactively and accurately diagnose problems, ultimately reducing maintenance time and fostering more effective human-robot collaboration.
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