State Estimation and Environment Recognition for Articulated Structures via Proximity Sensors Distributed Over the Whole Body

For robots with low rigidity, determining the robot's state based solely on kinematics is challenging. This is particularly crucial for a robot whose entire body is in contact with the environment, as accurate state estimation is essential for environmental interaction. We propose a method for simultaneous articulated robot posture estimation and environmental mapping by integrating data from proximity sensors distributed over the whole body. Our method extends the discrete-time model, typically used for state estimation, to the spatial direction of the articulated structure. The simulations demonstrate that this approach significantly reduces estimation errors.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-02-05
DOI
https://doi.org/10.1109/lra.2025.3539117
Citations
2
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
3.87
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article

State Estimation and Environment Recognition for Articulated Structures via Proximity Sensors Distributed Over the Whole Body

Kenji Tahara, Hikaru Arita, K. Iwao
2 citations
IEEE Robotics and Automation Letters
Hand Gesture Recognition Systems
3.87
article

State Estimation and Environment Recognition for Articulated Structures via Proximity Sensors Distributed Over the Whole Body

Kenji Tahara, Hikaru Arita, K. Iwao
article en
2 citations

Abstract

For robots with low rigidity, determining the robot's state based solely on kinematics is challenging. This is particularly crucial for a robot whose entire body is in contact with the environment, as accurate state estimation is essential for environmental interaction. We propose a method for simultaneous articulated robot posture estimation and environmental mapping by integrating data from proximity sensors distributed over the whole body. Our method extends the discrete-time model, typically used for state estimation, to the spatial direction of the articulated structure. The simulations demonstrate that this approach significantly reduces estimation errors.

IEEE Robotics and Automation LettersVol. 10(3)
Kyushu University (JP)
Openalex Percentile: Top 9%
Hand Gesture Recognition Systems
3.87
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