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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
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
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Items – Name: Title
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  Data: Prescient Collision-Free Navigation of Mobile Robots with Iterative Multimodal Motion Prediction of Dynamic Obstacles
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Ze%22">Zhang, Ze</searchLink>, 1995<br /><searchLink fieldCode="AR" term="%22Hajieghrary%2C+Hadi%22">Hajieghrary, Hadi</searchLink>, 1983<br /><searchLink fieldCode="AR" term="%22Dean%2C+Emmanuel%22">Dean, Emmanuel</searchLink>, 1976<br /><searchLink fieldCode="AR" term="%22Åkesson%2C+Knut%22">Åkesson, Knut</searchLink>, 1972
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  Data: <i>AIHURO-Intelligent människa-robot-samarbete IEEE Robotics and Automation Letters</i>. 8(9):5488-5495
– Name: Subject
  Label: Subject Terms
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  Data: <searchLink fieldCode="DE" term="%22Collision+avoidance%22">Collision avoidance</searchLink><br /><searchLink fieldCode="DE" term="%22deep+learning+methods%22">deep learning methods</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty%22">Uncertainty</searchLink><br /><searchLink fieldCode="DE" term="%22Vehicle+dynamics%22">Vehicle dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Trajectory%22">Trajectory</searchLink><br /><searchLink fieldCode="DE" term="%22Robots%22">Robots</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamics%22">Dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22autonomous+agents%22">autonomous agents</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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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  Data: electronic
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        Value: 10.1109/LRA.2023.3296333
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      – Text: English
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        PageCount: 8
        StartPage: 5488
    Subjects:
      – SubjectFull: Collision avoidance
        Type: general
      – SubjectFull: deep learning methods
        Type: general
      – SubjectFull: Uncertainty
        Type: general
      – SubjectFull: Vehicle dynamics
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      – SubjectFull: Trajectory
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      – SubjectFull: Robots
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      – SubjectFull: Mobile robots
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      – SubjectFull: Dynamics
        Type: general
      – SubjectFull: autonomous agents
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      – TitleFull: Prescient Collision-Free Navigation of Mobile Robots with Iterative Multimodal Motion Prediction of Dynamic Obstacles
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            NameFull: Zhang, Ze
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            NameFull: Dean, Emmanuel
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            NameFull: Åkesson, Knut
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              M: 01
              Type: published
              Y: 2023
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              Value: 9
          Titles:
            – TitleFull: AIHURO-Intelligent människa-robot-samarbete IEEE Robotics and Automation Letters
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