Collaborative human–machine interaction will be progressively intensified in industrial applications. The aim of this article is to examine current approaches to cobot safety by showing that these approaches can additionally benefit from systems thinking methods. The first part of this article covers a narrative literature review on predominantly techno-centric robot safety approaches, with a strong focus on containing kinetic energy and ensuring separation with humans. The second part introduces systems thinking methods to analyze a socio-technical perspective on cobot safety, including joint cognitive systems and distributed cognition perspectives. This explorative research dimension is expected to overcome an overly narrow interpretation of safety issues, anticipating the challenges ahead in ever more complex cobot applications. This article embraces a socio-technical perspective to explore the potential of Joint Cognitive Systems to manage risk and safety in cobot applications. Three systemic safety analysis approaches are presented and tested with a demonstrator case study concerning their feasibility for cobot applications: System-Theoretic Accident Model and Processes (STAMP); Functional Resonance Analysis Method (FRAM); and Event Analysis of Systemic Teamwork (EAST). These methods each provide interesting extensions to complement the traditional understanding of risk as required by current and future industrial cobot implementations. The power of systemic methods for safer and more efficient cobot operations lies in revealing the distributed and emergent result from joint actions and overcoming the reductionist view from individual failures or single agent responsibilities. The safe operation of cobot applications can only be achieved through alignment of design, training, and operation of such applications.

中文翻译：

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与工业协作机器人合作：安全分析的社会技术视角

协同人机交互将在工业应用中逐步加强。本文的目的是通过展示这些方法可以额外受益于系统思维方法来检查当前的协作机器人安全方法。本文的第一部分涵盖了主要以技术为中心的机器人安全方法的叙述性文献综述，重点关注包含动能和确保与人类分离。第二部分介绍了系统思维方法来分析协作机器人安全的社会技术视角，包括联合认知系统和分布式认知视角。这种探索性的研究维度有望克服对安全问题的过于狭隘的解释，预测越来越复杂的协作机器人应用面临的挑战。本文从社会技术角度探讨联合认知系统在管理协作机器人应用程序中的风险和安全方面的潜力。介绍了三种系统安全分析方法，并通过演示案例研究对其在协作机器人应用中的可行性进行了测试：系统理论事故模型和过程 (STAMP)；功能共振分析法（FRAM）；和系统团队合作（EAST）的事件分析。这些方法都提供了有趣的扩展，以补充当前和未来工业协作机器人实施所需的对风险的传统理解。更安全、更高效的协作机器人操作的系统方法的力量在于揭示联合行动的分布式和紧急结果，并克服个体故障或单一代理责任的简化观点。协作机器人应用程序的安全运行只能通过协调此类应用程序的设计、培训和操作来实现。