Mutual-cognition for proactive human-robot collaboration: A mixed reality-enabled visual reasoning-based method

Li, Shufei, You, Yingchao, Zheng, Pai, Wang, Xi Vincent and Wang, Lihui 2024. Mutual-cognition for proactive human-robot collaboration: A mixed reality-enabled visual reasoning-based method. IISE Transactions 56 (10) , pp. 1099-1111. 10.1080/24725854.2024.2313647

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Abstract

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Taylor and Francis Group
ISSN:	2472-5854
Date of First Compliant Deposit:	19 April 2024
Date of Acceptance:	22 January 2024
Last Modified:	14 Aug 2024 10:15
URI:	https://orca.cardiff.ac.uk/id/eprint/166836

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