人間とロボットの協調のための知的フレームワーク：動的エルゴノミクスと適応的意思決定
Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making
視覚認識・エルゴノミクス監視・適応的行動決定を統合し、作業者の安全と効率を両立させる人間-ロボット協調フレームワークを提案。実験で高精度な意図認識と低遅延のリスク分類・介入決定を実証した。
著者: Francesco Iodice, Elena De Momi, Arash Ajoudani
分類: cs.RO, cs.CV, cs.HC, cs.LG
原文アブストラクト
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.
