提升机器人拧螺栓的可靠性,实现故障下的安全操作
Improving dependability in robotized bolting operations
- 构建可依赖的控制框架,支持全程精确扭矩与主动柔顺
- 实测中显著提升故障检测能力与操作员态势感知
- 适合工业装配与科研设备维护中对安全性要求高的场景
螺栓连接在工业装配及科学设施维护中至关重要,需高精度和强容错能力。尽管机器人方案能提升安全性和效率,现有系统仍缺乏可靠的自主性与故障管理能力。为此,本文提出一种可依赖的机器人拧螺栓控制框架,并在具体机器人系统上实现。该系统通过控制架构实现全程精确扭矩控制与主动柔顺,确保故障下仍能安全交互。设计多模态人机界面(HRI),实时可视化关键信息,支持自动与手动模式无缝切换,提升操作员态势感知与故障发现能力。高层监督器(SV)协调任务执行并管理控制模式切换,遵循监督控制(SVC)范式,同时保障人工操作员决策权。系统在管道法兰连接这一典型任务中验证,涵盖多种故障场景。结果表明,故障检测能力提升,操作员态势感知增强,且螺栓操作精准合规。但实验也揭示仅依赖单个摄像头难以实现全面态势感知。
原文摘要 · Abstract (English)
Bolting operations are critical in industrial assembly and in the maintenance of scientific facilities, requiring high precision and robustness to faults. Although robotic solutions have the potential to improve operational safety and effectiveness, current systems still lack reliable autonomy and fault management capabilities. To address this gap, we propose a control framework for dependable robotized bolting tasks and instantiate it on a specific robotic system. The system features a control architecture ensuring accurate driving torque control and active compliance throughout the entire operation, enabling safe interaction even under fault conditions. By designing a multimodal human-robot interface (HRI) providing real-time visualization of relevant system information and supporting seamless transitions between automatic and manual control, we improve operator situation awareness and fault detection capabilities. A high-level supervisor (SV) coordinates the execution and manages transitions between control modes, ensuring consistency with the supervisory control (SVC) paradigm, while preserving the human operator's authority. The system is validated in a representative bolting operation involving pipe flange joining, under several fault conditions. The results demonstrate improved fault detection capabilities, enhanced operator situational awareness, and accurate and compliant execution of the bolting operation. However, they also reveal the limitations of relying on a single camera to achieve full situational awareness.
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