Comparative Analysis Of Mental Workload In Adaptive Human-Robot Collaboration During Assembly Tasks

Abstract

One of the most rapidly evolving aspects of this digital transformation is the increasingly advanced collaboration between humans and machines. Collaborative robots, or cobots, are especially advantageous and most used in assembly tasks, where the high payload and repeatability characterizing traditional robotic systems need to be combined with the skills and flexibility of human operators. The work showed that during the collaboration between cobots and operators, the so-called Human-Robot Collaboration (or HRC), allows for improved productivity and a reduced mental workload in industrial manufacturing assembly tasks. The paper analyses the mental workload and productivity of an assembly task where participants performed the task in three different scenarios: a) assembly task without the robot (standard scenario); b) assembly task with the robot (collaborative scenario); c) assembly task with the robot and guided throughout the task (guided collaborative scenario). The analysis of mental workload and productivity is shown in three different time periods for three consecutive halves of analysis during the task, each lasting 30 minutes. The analysis was conducted for three participants. The results show how mental workload decreased from the first to the third scenario while the productivity index increased. The analysis of mental workload was performed through the innovative electroencephalogram (EEG) sensor cap, while the analysis of productivity was performed through observational methods (checklist).