Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915241773760512 |
|---|---|
| author | Iodice, Francesco De Momi, Elena Ajoudani, Arash |
| author_facet | Iodice, Francesco De Momi, Elena Ajoudani, Arash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07901 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making Iodice, Francesco De Momi, Elena Ajoudani, Arash Robotics Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 68T40, 68T45, 93C85 I.2.9; H.5.2; I.2.10 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. |
| title | Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making |
| topic | Robotics Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 68T40, 68T45, 93C85 I.2.9; H.5.2; I.2.10 |
| url | https://arxiv.org/abs/2503.07901 |