Model Predictive Trajectory Planning for Human-Robot Handovers
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916201226043392 |
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| author | Oelerich, Thies Hartl-Nesic, Christian Kugi, Andreas |
| author_facet | Oelerich, Thies Hartl-Nesic, Christian Kugi, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07505 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model Predictive Trajectory Planning for Human-Robot Handovers Oelerich, Thies Hartl-Nesic, Christian Kugi, Andreas Robotics 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. |
| title | Model Predictive Trajectory Planning for Human-Robot Handovers |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.07505 |