Turn-taking prediction for human-robot collaborative assembly considering human uncertainty

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-08-23 DOI:10.1115/1.4063231
Wenjun Xu, Siqi Feng, Bitao Yao, Zhenrui Ji, Zhihao Liu
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Abstract

Human-robot collaboration (HRC) combines the repeatability and strength of robots and human's ability of cognition and planning to enable a flexible and efficient production mode. The ideal HRC process is that robots can smoothly assist workers in complex environments. This means that robots need to know the process's turn-taking earlier, adapt to the operating habits of different workers, and make reasonable plans in advance to improve the fluency of HRC. However, many of the current HRC systems ignore the fluent turn-taking between robots and humans, which results in unsatisfactory HRC and affects productivity. Moreover, there are uncertainties in humans as different humans have different operating proficiency, resulting in different operating speeds. This requires the robots to be able to make early predictions of turn-taking even when human is uncertain. Therefore, in this paper, an early turn-taking prediction method in HRC assembly tasks with Izhi neuron model-based spiking neuron network (SNN) is proposed. On this basis, dynamic motion primitives (DMP) are used to establish trajectory templates at different operating speeds. The length of the sequence sent to the SNN network is judged by the matching degree between the observed data and the template, so as to adjust to human uncertainty. The proposed method is verified by the gear assembly case. The results show that our method can shorten the human-robot turn-taking recognition time under human uncertainty.
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考虑人为不确定性的人-机器人协同装配转弯预测
人机协作(HRC)结合了机器人的可重复性和强度以及人类的认知和规划能力,实现了灵活高效的生产模式。理想的HRC过程是机器人可以在复杂的环境中顺利地帮助工人。这意味着机器人需要更早地了解流程的轮次,适应不同工人的操作习惯,并提前制定合理的计划,以提高HRC的流畅性。然而,目前的许多HRC系统忽视了机器人和人类之间流畅的转弯,这导致了不令人满意的HRC并影响了生产力。此外,由于不同的人有不同的操作熟练度,导致不同的操作速度,因此人类也存在不确定性。这就要求机器人即使在人类不确定的情况下也能对转弯做出早期预测。因此,本文提出了一种基于Izhi神经元模型的尖峰神经元网络(SNN)的HRC装配任务早期转弯预测方法。在此基础上,使用动态运动基元(DMP)建立不同操作速度下的轨迹模板。发送到SNN网络的序列长度是根据观测数据与模板的匹配程度来判断的,以适应人类的不确定性。通过齿轮装配实例验证了该方法的有效性。结果表明,该方法能够在人类不确定的情况下缩短人机转弯识别时间。
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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