Modelling human satisfaction in amr-assisted order picking: an experimental exploratory study of motivation–workload trade-offs

Human-robot interaction in warehouse order picking is increasingly adopted in workplaces. One of the most common robotic solutions in warehouses is autonomous mobile robot (AMR). While AMRs improve efficiency and reduce physical strain, their impact on human satisfaction remains complex, involving trade-offs between motivation and workload. This paper proposes a mathematical model of human satisfaction in AMR-assisted order picking, conceptualizing satisfaction as the difference between motivational task components and perceived workload. The model is evaluated using a within-subjects experiment where participants completed order picking manually and with AMR assistance. Additionally, a simulation scenario is implemented to demonstrate how the proposed satisfaction function can be embedded into warehouse system design and evaluated under alternative AMR deployment strategies. Results demonstrate shifts in satisfaction factors across conditions and show the model’s utility for understanding satisfaction outcomes under different task allocations. However, the exploratory nature of the experiment and the limited sample size, constrains the generalisability of the empirical findings and highlight the need for further validation in larger and operational warehouse settings. This study bridges subjective experience and system design, offering a tool for human-centric simulation and decision support.