提出一套动态适应的人机协作框架，提升工业场景下的安全与效率。
Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making
- 融合视觉感知、人体工学监测与自适应决策树实现协同控制。
- 识别操作意图准确率达92.5%，风险评估延迟仅0.57秒。
- 适合智能制造、人机协作系统研发人员参考。
协作机器人在工业环境中的应用提升了生产效率，但也带来了操作员安全与人体工学方面的挑战。本文提出一种创新框架，整合先进视觉感知、持续人体工学监测与自适应行为树决策机制，克服了传统方法中各模块孤立运作的局限。该方案将深度学习模型、先进追踪算法与动态人体工学评估结合，构成模块化、可扩展且自适应的系统。实验验证表明，该框架在多个维度优于现有方案：视觉感知模块在检测任务中取得72.4% mAP@50:95；操作意图识别准确率达92.5%；风险分类延迟仅为0.57秒；机器人干预决策响应时间仅0.07秒，相较基准系统提升56%。该综合方案为工业环境中的人机协作提供了高安全性、高效能与实时适应性的可靠平台。
原文摘要 · Abstract (English)
The integration of collaborative robots into industrial environments has improved productivity, but has also highlighted significant challenges related to operator safety and ergonomics. This paper proposes an innovative framework that integrates advanced visual perception, continuous ergonomic monitoring, and adaptive Behaviour Tree decision-making to overcome the limitations of traditional methods that typically operate as isolated components. Our approach synthesizes deep learning models, advanced tracking algorithms, and dynamic ergonomic assessments into a modular, scalable, and adaptive system. Experimental validation demonstrates the framework's superiority over existing solutions across multiple dimensions: the visual perception module outperformed previous detection models with 72.4% mAP@50:95; the system achieved high accuracy in recognizing operator intentions (92.5%); it promptly classified ergonomic risks with minimal latency (0.57 seconds); and it dynamically managed robotic interventions with exceptionally responsive decision-making capabilities (0.07 seconds), representing a 56% improvement over benchmark systems. This comprehensive solution provides a robust platform for enhancing human-robot collaboration in industrial environments by prioritizing ergonomic safety, operational efficiency, and real-time adaptability.
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