Model Predictive Trajectory Planning for Human-Robot Handovers
<|reference_start|>Model Predictive Trajectory Planning for Human-Robot Handovers: This work develops a novel trajectory planner for human-robot handovers. The handover requirements can naturally be handled by a path-following-based model predictive controller, where the path progress serves as a progress measure of the handover. Moreover, the deviations from the path are used to follow human motion by adapting the path deviation bounds with a handover location prediction. A Gaussian process regression model, which is trained on known handover trajectories, is employed for this prediction. Experiments with a collaborative 7-DoF robotic manipulator show the effectiveness and versatility of the proposed approach.<|reference_end|>
@article{oelerich2024model,
    title={Model Predictive Trajectory Planning for Human-Robot Handovers},
    author={Thies Oelerich, Christian Hartl-Nesic and Andreas Kugi},
    journal={Proceedings of the VDI Mechatroniktagung 2024, pp 65-72},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07505},
    primaryClass={cs.RO}
}