3-D Fitts' Law for Performance Prediction of Human–Machine Teaming

Human machine teaming (HMT) applications, bolstered by advancements in control technology, are proliferating across industries. Despite this, accurately predicting HMT performance and time efficiency, particularly in open and potentially hazardous environments, remains challenging due to the complexity of modeling mission difficulty and human behavior. This study tackles the challenge by extending cursor-based Fitts' law into a 3-D context with six degrees of freedom. This represents the first attempt to model task difficulty in HMT by integrating both target translational and orientational components, effectively expanding Fitts' law beyond human–computer interaction to robots and machinery. This novel approach is tailored for HMT applications and tested through an excavator simulation and a quadruped manipulator robot experiment. The study showcases our model's capability to predict the performance of complex, multistep HMT tasks accurately. This research opens a novel pathway in HMT performance prediction, offering promising implications for real-world applications.