Communicating robots’ intent through visual cues enhances human anticipatory behavior in human–dual robot collaboration

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Robotics and Computer-integrated Manufacturing Pub Date : 2024-10-03 DOI:10.1016/j.rcim.2024.102886
Loizos Psarakis, Dimitris Nathanael, Nicolas Marmaras
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Abstract

The present study aims at exploring the effect of communicating robots’ intent through visual cues, to the human on a complex human-robot collaborative task. Specifically, it aims to investigate (i) whether the use of such “anticipatory cues” will have a positive effect on task efficiency, human safety and collaborating fluency, (ii) the degree of this effect with varying robots’ speed and (iii) whether a retention effect will be observed after the removal of the cues. For exploring these issues, a human - dual robot industrial assembly task was designed in a Virtual Reality simulation environment and testing was carried out by 64 volunteer participants. Results showed that communicating robots’ intent through visual cues enhanced human anticipatory behavior, resulting in a significant improvement in human safety, team efficiency and collaborative fluency, in conjunction with a favorable subjective tendency towards the robots. However, the positive effect of the anticipatory cues was not found to increase with higher robot speed. Finally, the findings suggest that prior exposure to the cues made participants more confident in coordinating with the robots, even when the cues were removed from them, thus retaining their prior efficiency but with a negative effect on safety. In summary, the study provides evidence that use of anticipatory visual cues accelerates the legibility of robot movement and fosters human confidence and familiarization. The use of anticipatory cues seems promising for high-pace, non-repetitive interactions with collaborative robots or as a training aid in more repetitive human-robot collaborative tasks.
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通过视觉提示传达机器人意图,增强人类与双机器人协作中的预期行为
本研究旨在探索在复杂的人机协作任务中,通过视觉提示向人类传达机器人意图的效果。具体来说,本研究旨在探讨:(i) 使用这种 "预期提示 "是否会对任务效率、人类安全和协作流畅性产生积极影响;(ii) 随着机器人速度的变化,这种影响的程度;(iii) 消除提示后是否会观察到保留效应。为了探讨这些问题,我们在虚拟现实模拟环境中设计了一项人类-双机器人工业装配任务,并由 64 名自愿参与者进行了测试。结果表明,通过视觉提示传达机器人的意图增强了人类的预期行为,从而显著提高了人类的安全性、团队效率和协作流畅性,同时也使人们对机器人产生了良好的主观倾向。然而,预期提示的积极作用并没有随着机器人速度的提高而增强。最后,研究结果表明,事先接触这些提示会让参与者在与机器人协调时更有信心,即使从他们身上移除这些提示也是如此,从而保留了他们先前的效率,但对安全性产生了负面影响。总之,这项研究提供的证据表明,使用预期视觉提示可加快机器人动作的可读性,并增强人类的信心和熟悉度。在与协作机器人进行高节奏、非重复性互动时,或在执行重复性较高的人机协作任务时,使用预期提示似乎大有可为。
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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