Adaptive Electronic Skin Sensitivity for Safe Human-Robot Interaction
<|reference_start|>Adaptive Electronic Skin Sensitivity for Safe Human-Robot Interaction: Artificial electronic skins covering complete robot bodies can make physical human-robot collaboration safe and hence possible. Standards for collaborative robots (e.g., ISO/TS 15066) prescribe permissible forces and pressures during contacts with the human body. These characteristics of the collision depend on the speed of the colliding robot link but also on its effective mass. Thus, to warrant contacts complying with the Power and Force Limiting (PFL) collaborative regime but at the same time maximizing productivity, protective skin thresholds should be set individually for different parts of the robot bodies and dynamically on the run. Here we present and empirically evaluate four scenarios: (a) static and uniform - fixed thresholds for the whole skin, (b) static but different settings for robot body parts, (c) dynamically set based on every link velocity, (d) dynamically set based on effective mass of every robot link. We perform experiments in simulation and on a real 6-axis collaborative robot arm (UR10e) completely covered with sensitive skin (AIRSKIN) comprising eleven individual pads. On a mock pick-and-place scenario with transient collisions with the robot body parts and two collision reactions (stop and avoid), we demonstrate the boost in productivity in going from the most conservative setting of the skin thresholds (a) to the most adaptive setting (d). The threshold settings for every skin pad are adapted with a frequency of 25 Hz. This work can be easily extended for platforms with more degrees of freedom and larger skin coverage (humanoids) and to social human-robot interaction scenarios where contacts with the robot will be used for communication.<|reference_end|>
@article{rustler2024adaptive,
    title={Adaptive Electronic Skin Sensitivity for Safe Human-Robot Interaction},
    author={Lukas Rustler, Matej Misar, Matej Hoffmann},
    journal={arXiv preprint arXiv:2409.06369},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06369},
    primaryClass={cs.RO}
}