Lead Institution: Cranfield University
Industry Partners: Airbus
Cranfield University: Industrial Psychology and Human Factors (IPHF) Group
Sarah Fletcher, Senior Research Fellow
Teegan Johnson, Research Assistant (ECR)
Jose Gonzalez-Domingo, Research Fellow (ECR) based full time at Airbus
John Thrower, Senior Technical Offiicer
Advanced Manufacturing Research Centre: Integrated Manufacturing Group
Ben Morgan, Head of IMG
TBC, Research Engineer (ECR)
Katie Dodds-Hughes, Senior Ergonomist
Florian Carle, Ergonomist
Project Duration: 12 months (March 2018 – March 2019)
In the current climate of manufacturing digitisation and automation, the implementation of human-robot collaboration (HRC) systems in production processes is escalating rapidly, not simply to replace workers with robots but to exploit the strengths of each for better performance. Whereas traditional heavy industrial robots had to be segregated from workers in the past, advances in sensor based safety monitoring mean it is now possible to integration people and robots more closely within the same shared space and time to complete industrial tasks. However, we know that closer proximity and interactions with robots tend to induce human reactions that may impact on performance. For example, cognitive perceptions of risk / trust influence user acceptance, cognitive workload, and efficiency. We also know that perceived levels of risk / trust are based on people’s interpretations of specific robot features, like speed and motion. Unfortunately, there is currently no practical method for considering these relationships in the design of new HRC systems.
We have long known that if human factors are not considered sufficiently, and at an early stage of design, the operational success of new manufacturing technologies is compromised. Hence, a degree of physical ergonomic analysis is now commonplace in the design of new human-in-the-loop systems using digital human modelling analysis tools (DHM) in computer aided design (CAD) packages. However, these tools currently do not yet offer any cognitive / behavioural analysis. Thus, although CAD modelling is used widely in industrial design to predict optimal conditions and prevent costly post-implementation issues, it does not yet offer the human analysis capability for incorporating cognitive / behavioural data in order to predict the likely impacts of HRC system features on human responses and performance.
This feasibility study is designed to test the viability of integrating human behavioural rule data into a DHM format specifically for the design of industrial human-robot collaboration systems as this is a clear and significant current gap in the state of the art for manufacturing system design.