用语音指挥机器人协作，让工厂操作更省时省力。
EBuddy: a workflow orchestrator for industrial human-machine collaboration
- 将专家经验转为语音可交互的状态机流程
- 工业测试中全流程耗时显著降低，重复性好且操作负担轻
- 适合需要人机协同的制造场景，如机器人辅助修复
本文提出EBuddy，一种面向工业环境中自然人机协作的语音引导工作流协调器。针对工具密集型流程中专家经验难规模化、操作质量随人员和会话变化而下降的问题，EBuddy将专家实践建模为基于有限状态机（FSM）的应用程序，在运行时提供可解释的决策框架（当前状态与允许动作），使语音请求在状态约束下被理解，并驱动系统执行和监控相应工具交互。通过模块化工作流组件，EBuddy协调包括图形界面软件和协作机器人在内的异构资源，实现全程语音交互，依托自动语音识别与意图理解技术。在定向能量沉积（DED）叶轮叶片检测与修复准备的人机协同工业试点中，实现了从入职培训、3D扫描处理到修复程序生成全流程的显著缩短，同时保持高重复性与低操作负担。
原文摘要 · Abstract (English)
This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets a recurrent bottleneck in tool-intensive workflows: expert know-how is effective but difficult to scale, and execution quality degrades when procedures are reconstructed ad hoc across operators and sessions. EBuddy operationalizes expert practice as a finite state machine (FSM) driven application that provides an interpretable decision frame at runtime (current state and admissible actions), so that spoken requests are interpreted within state-grounded constraints, while the system executes and monitors the corresponding tool interactions. Through modular workflow artifacts, EBuddy coordinates heterogeneous resources, including GUI-driven software and a collaborative robot, leveraging fully voice-based interaction through automatic speech recognition and intent understanding. An industrial pilot on impeller blade inspection and repair preparation for directed energy deposition (DED), realized by human-robot collaboration, shows substantial reductions in end-to-end process duration across onboarding, 3D scanning and processing, and repair program generation, while preserving repeatability and low operator burden.
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