This study presents an integrated experimental and modeling framework to investigate human–robot collision dynamics involving a collaborative manipulator (KUKA LBR iiwa 14 R820). A dedicated impact test prototype was developed to reproduce controlled contact scenarios between the robot and human body analogues under various dynamic conditions. The experimental setup enables the acquisition of synchronized force, velocities, and displacement signals during contact events. These data are used to calibrate and validate a set of contact models, ranging from classical formulations such as Hertz and Hunt–Crossley to more recent supervised machine learning models. The proposed methodology allows a quantitative assessment of model accuracy and physical consistency in replicating real collision phenomena. Furthermore, the effective mass of the robot along its kinematic chain is estimated to compute impact energy and predict the interaction severity according to ISO 10218-1/2:2025 safety limits. The results highlight the trade-off between model complexity and predictive capability, offering alternative guidelines for collision severity evaluation in collaborative robotics applications.
Caneschi et al. (Thu,) studied this question.