Prescient Collision-Free Navigation of Mobile Robots with Iterative Multimodal Motion Prediction of Dynamic Obstacles
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| Název: | Prescient Collision-Free Navigation of Mobile Robots with Iterative Multimodal Motion Prediction of Dynamic Obstacles |
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| Autoři: | Zhang, Ze, 1995, Hajieghrary, Hadi, 1983, Dean, Emmanuel, 1976, Åkesson, Knut, 1972 |
| Zdroj: | AIHURO-Intelligent människa-robot-samarbete IEEE Robotics and Automation Letters. 8(9):5488-5495 |
| Témata: | Collision avoidance, deep learning methods, Uncertainty, Vehicle dynamics, Trajectory, Robots, Mobile robots, Dynamics, autonomous agents |
| Popis: | To explore safe interactions between a mobile robot and dynamic obstacles, this paper presents a comprehensive approach to collision-free navigation in dynamic indoor environments. The approach integrates multimodal motion predictions of dynamic obstacles with predictive control for obstacle avoidance. Multimodal Motion Prediction (MMP) is achieved by a deep-learning method that predicts multiple plausible future positions. By repeating the MMP for each time offset in the future, multi-time-step MMPs are obtained. A nonlinear Model Predictive Control (MPC) solver uses the prediction outcomes to achieve collision-free trajectory tracking for the mobile robot. The proposed integration of multimodal motion prediction and trajectory tracking outperforms other non-deep-learning methods in complex scenarios. The approach enables safe interaction between the mobile robot and stochastic dynamic obstacles. |
| Popis souboru: | electronic |
| Přístupová URL adresa: | https://research.chalmers.se/publication/536777 https://research.chalmers.se/publication/536777/file/536777_Fulltext.pdf |
| Databáze: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | To explore safe interactions between a mobile robot and dynamic obstacles, this paper presents a comprehensive approach to collision-free navigation in dynamic indoor environments. The approach integrates multimodal motion predictions of dynamic obstacles with predictive control for obstacle avoidance. Multimodal Motion Prediction (MMP) is achieved by a deep-learning method that predicts multiple plausible future positions. By repeating the MMP for each time offset in the future, multi-time-step MMPs are obtained. A nonlinear Model Predictive Control (MPC) solver uses the prediction outcomes to achieve collision-free trajectory tracking for the mobile robot. The proposed integration of multimodal motion prediction and trajectory tracking outperforms other non-deep-learning methods in complex scenarios. The approach enables safe interaction between the mobile robot and stochastic dynamic obstacles. |
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| ISSN: | 23773766 |
| DOI: | 10.1109/LRA.2023.3296333 |