Development of a Systemic Safety Assessment Method for Human-Robot Interdependencies

Understanding and managing risks in collaborative robot applications requires switching between micro, meso, and macro understandings of systems. This perspective justifies the need for hybrid methods that are able to investigate socio-technical interactions at different abstraction levels, and across different system lifecycle stages. This thesis aims to develop a comprehensive framework for socio-technical safety modelling, which embraces a functional perspective to focus on interdependencies among complex human-machine systems. The proposed FRAM-IA framework relies on the Functional Resonance Analysis Method (FRAM) and extends it by introducing a systematic functional Interdependency Analysis (IA). The theoretical framework being developed is tested and validated with two case studies, each in different application domains and lifecycle stages. One study involves collaborative robot warehouse operations in an operational setting. Another case study applied the developed framework to the design of a collaborative surgical robot. Two types of validation workshops, each for a different target audience were organised. Academic and industrial experts participated in a separate validation exercise to test the feasibility of learning the newly developed framework and applying it into practice. A FRAM-IA instruction guide was subsequently developed and refined with feedback from the validation workshop participants to foster the application of the framework across diverse types of collaborative applications in different domains.