Abstract
AbstractCollaborative 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.
Highlights
Collaborative robots perform tasks in collaboration with human workers within the scope of an industrial setting (Gualtieri et al, 2021)
The literature review on collaborative robots presented in this publication revealed a great emphasis on a techno‐centric perspective, whereby risk was narrowly defined in terms of uncontained energy, with a typical focus on safety mitigation in terms of speed, kinetic energy, and separation
Collaborative robot applications purposefully use the principle of distributed cognition to the advantage of a joint action that is stronger than the sum of its parts, which motivated to examine the problem domain from a Joint Cognitive Systems (JCS) perspective
Summary
Collaborative robots perform tasks in collaboration with human workers within the scope of an industrial setting (Gualtieri et al, 2021). Traditional robots were regulated by several regulations in which separation between industrial robots and humans was rigidly prescribed This conflicts with the very nature of collaborative workspaces. Some examples (El Zaatari et al, 2019) are tasks such as (i) co‐manipulation where a human guides an object path while the cobot supports the weight of the object; (ii) humans inserting bolts in a plate while a cobot tightens these bolts from the opposite side of the plate; or (iii) assembly actions that are dynamically distributed between humans and cobots according to workload and energy consumption. New forms of collaboration emerge, for example by the combination of mobile bases with collaborative manipulation robots (Hentout et al, 2019; Unger et al, 2018) These technologies introduce for more versatility, which confronts designers with understanding the joint behavior of both technologies
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