Research on Guidance Methods of Digital Twin Robotic Arms Based on User Interaction Experience Quantification

Abstract

The evolution of the manufacturing sector coupled with advancements in digital twin technology has precipitated the extensive integration of digital twin robotic arms within the industrial domain. Notwithstanding this trend, there exists a paucity of studies examining the interaction of these robotic arms in virtual reality (VR) contexts from the user’s standpoint. This paper delves into the virtual interaction of digital twin robotic arms by concentrating on effective guidance methodologies for the input of their target motion trajectories. Such a focus is pivotal to optimize input precision and efficiency, thus contributing to research on the virtual interaction interfaces of these robotic arms. During empirical evaluations, metrics related to human–machine interaction, such as objective operational efficiency, precision, and subjective workload, were meticulously quantified. Moreover, the influence of disparate guidance methods on the interaction experience of digital twin robotic arms and their corresponding scenarios was investigated. Consequent findings offer pivotal insights regarding the efficacy of these guidance methods across various scenarios, thereby serving as an invaluable guide for future endeavors aiming to bolster interactive experiences in devices akin to digital twin robotic arms.