Early Model-Based Safety Analysis for Collaborative Robotic Systems

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2024-08-14 DOI:10.1109/TASE.2024.3440048
Meenakshi Manjunath;Jeshwitha Jesus Raja;Marian Daun
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Abstract

The current era is marked by an accelerated digitization of manufacturing processes, with robotic systems increasingly integrated into various workflows. Yet, despite significant advancements, it is impractical to fully automate certain tasks due to prohibitive costs and technical constraints. As a result, there’s a growing emphasis on human-robot collaboration (HRC) for intricate operations. In HRC scenarios, humans and robots co-inhabit the same work environment, operating side by side. More than just mere coexistence in the same space, they actively collaborate on shared tasks, thus raising the stakes in terms of safety. The dynamic behavior of robots must be synchronized with the anticipated and unexpected human actions, adding another layer of complexity to the safety considerations. It is essential to conduct comprehensive safety analyses that identify potential risks that pose harm to the human operator. As a proactive measure to foster early-stage safety and risk analysis, we propose the use of goal models. The approach enables the specification of safety threats within the HRC context, thereby facilitating the development of safety tasks and supportive monitoring mechanisms. This approach helps in the refinement and implementation of safety measures, ensuring a secure and productive environment for human-robot collaboration. Note to Practitioners—This paper was motivated by the need for conducting thorough safety analyses in future manufacturing scenarios. This is particularly the case as the use of HRC becomes more common in industry. This is fostered by the ideas of smart manufacturing and the spread of collaborative robots (cobots) in work environments. To support proper safety analyses in early phases of development, we propose a model-based methodology to relate the requirements of the system with the corresponding safety hazards and its countermeasures. The proposed model-based approach places particular emphasis on the identification of hazards not only related to a single system but resulting from collaboration scenarios. Thus, it contributes to an extensive safety analysis of collaborative robotic systems right in the early development stages. Application to a manufacturing case example showed its applicability and usefulness to identify potential safety hazards early in development. Results, however, also indicate that future work is needed for implementing a seamless view concept allowing the derivation of models representing specific situations to be closely investigated. For this, abstraction mechanism can aid in guiding the safety analysis procedure.
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基于模型的协作机器人系统早期安全分析
当前时代的标志是制造过程的加速数字化,机器人系统越来越多地集成到各种工作流程中。然而,尽管取得了重大进展,由于高昂的成本和技术限制,完全自动化某些任务是不切实际的。因此,对于复杂的操作,人们越来越重视人机协作(HRC)。在HRC场景中,人类和机器人共同生活在相同的工作环境中,肩并肩地工作。他们不仅仅是在同一个空间里共存,他们还积极合作完成共同的任务,从而提高了安全方面的风险。机器人的动态行为必须与预期和意外的人类行为同步,这为安全考虑增加了另一层复杂性。必须进行全面的安全分析,以识别对操作人员造成伤害的潜在风险。作为促进早期安全和风险分析的积极措施,我们建议使用目标模型。该方法能够在人权理事会范围内详细说明安全威胁,从而促进安全任务和支持性监测机制的发展。这种方法有助于改进和实施安全措施,确保为人机协作提供安全和高效的环境。给从业人员的说明——本文的动机是需要在未来的制造场景中进行彻底的安全分析。随着HRC在工业中的使用越来越普遍,情况尤其如此。这是由智能制造的理念和协作机器人(cobots)在工作环境中的普及所促进的。为了支持在开发的早期阶段进行适当的安全分析,我们提出了一种基于模型的方法,将系统的需求与相应的安全隐患及其对策联系起来。提出的基于模型的方法特别强调识别不仅与单个系统有关的危害,而且与协作场景有关的危害。因此,它有助于在早期开发阶段对协作机器人系统进行广泛的安全分析。通过对一个制造案例的应用,证明了该方法在开发早期识别潜在安全隐患方面的适用性和有效性。然而,结果也表明,需要进一步的工作来实现无缝视图概念,以便对代表特定情况的模型的推导进行仔细研究。为此,抽象机制有助于指导安全分析过程。
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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