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A comprehensive safety architecture for human–robot collaboration in confined workspaces using improved artificial potential field

Published online by Cambridge University Press:  28 March 2025

Darren Alton Dsouza Open the ORCID record for Darren Alton Dsouza [Opens in a new window] ,
Shravan Shenoy ,
Mingfeng Wang Open the ORCID record for Mingfeng Wang [Opens in a new window]  and
Abhra Roy Chowdhury Open the ORCID record for Abhra Roy Chowdhury [Opens in a new window]
Darren Alton Dsouza
Affiliation:
Department of Design and Manufacturing, Indian Institute of Science Campus, Gulmohar Marg, Devasandra Layout, Bengaluru, Karnataka, India
Shravan Shenoy
Affiliation:
Department of Design and Manufacturing, Indian Institute of Science Campus, Gulmohar Marg, Devasandra Layout, Bengaluru, Karnataka, India
Mingfeng Wang
Affiliation:
Department of Mechanical and Aerospace Engineering, Brunel University London, Kingston Lane, Uxbridge, UK
Abhra Roy Chowdhury*
Affiliation:
Department of Design and Manufacturing, Indian Institute of Science Campus, Gulmohar Marg, Devasandra Layout, Bengaluru, Karnataka, India
*
Corresponding author: Abhra Roy Chowdhury; Email: [email protected]
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Abstract

Collaborative robotics in manufacturing introduces a new era of seamless human–robot collaboration (HRC), enhancing production line efficiency and adaptability. However, guaranteeing safe interaction while maintaining performance objectives presents significant challenges. Integrating safety with optimal robot performance is paramount to minimize task time and ensure its completion. Our work introduces an architecture for safety in confined human–robot workspaces by integrating existing safety and productivity methods into a unified framework specifically designed for constrained environments. By employing an improved artificial potential field, we optimize paths based on length and bending energy and compare baseline algorithms like gradient descent algorithm and rapidly exploring random tree (RRT*). We propose an evaluation metric for system performance that objectively maps to the system’s safety and efficiency in diverse collaborative scenarios. Additionally, the architecture supports multimodal interaction, including gesture-based inputs, for intuitive control and improved operator experience. Safety measures address static and dynamic obstacles using potential fields and safety zones, with a real-time safety evaluation module adjusting trajectories under specified constraints. A performance recovery algorithm facilitates swift resumption of high-speed operations post safety interventions. Validation includes comparing the algorithmic performance through simulations and experiments using the 6-degrees of freedom UR5 robot by universal robots to identify the most suitable algorithm. Results demonstrate an 83.87% improvement in system performance compared to ideal case scenarios, validating the effectiveness of the proposed architecture, evaluation metric, and multimodal interaction in enhancing safety and productivity.

Keywords

human–robot collaborationsafe motion planningproductivityimproved artificial potential fieldsindustrial automation
Type
Research Article
Information
Robotica , First View , pp. 1 - 21
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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