Optimising Train Axle Inspection with the Implementation of Human-Robot Collaboration: A Human Factors Perspective

Train axle inspection tasks rely on human operators working in and around depots and pits, searching for and identifying defects, with the use of non-destructive testing methods. Characteristics of the working environment and equipment design increase the likelihood of human error due to a variety of issues such as poor lighting, restricted access, awkward postures and limitations associated with the design of equipment interfaces. The purpose of this discussion paper is to encourage debate amongst the rail industry by proposing a new concept for carrying out this task, namely human-robot collaboration. Literature review from comparable domains has identified that human-collaborative robotic systems have been proposed and developed to optimise asset health monitoring. This concept could potentially be applied to train axle inspection with profound implications. At the same time, it is acknowledged that integrating humans and robotic systems within the same workspace will be a significant challenge and will require a holistic human factors approach. To this end, the latest empirical research on the key human factors that need to be considered, at an organisational and individual level, for the successful integration of humans and robots in industrial applications is presented.