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September 10, 2025MachinesOpen Access

An NMPC-ECBF Framework for Dynamic Motion Planning and Execution in Vision-Based Human–Robot Collaboration

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Authors

DZDianhao ZhangQueen's University Belfast MVMien VanQueen's University Belfast PSPantelis SopasakisQueen's University Belfast

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Implication

Framework integrates nonlinear model predictive control and safety features for real-time motion planning, suggesting effective human-robot collaboration.

Key Points

The proposed framework achieved a 23.2% reduction in execution time during human-robot collaboration tasks, indicating significant efficiency improvements.
Integration of a vision system with nonlinear model predictive control allows for effective path planning and improves safety measures during collaboration.
Utilizing exponential control barrier function enhances the safety of dynamic motion planning in heterogeneous environments involving robots and humans.
Experimental validation showed positive results for various human-robot assembly subtasks, emphasizing the practicality of the proposed approach.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68c1b34d54b1d3bfb60e9b2a https://doi.org/10.3390/machines13080672

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Implication

Framework integrates nonlinear model predictive control and safety features for real-time motion planning, suggesting effective human-robot collaboration.

An NMPC-ECBF Framework for Dynamic Motion Planning and Execution in Vision-Based Human–Robot Collaboration

Discussion

Member takes

Implication

Key Points

Cite This Study

An NMPC-ECBF Framework for Dynamic Motion Planning and Execution in Vision-Based Human–Robot Collaboration

Discussion

Member takes

Implication

Key Points

Cite This Study

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