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    Probabilistic foot contact estimation by fusing information from dynamics and differential/forward kinematics  
    Hwangbo, Jemin(ETH Zurich)
    Korea | IROS 2016
    2016-10-09 | 바로가기
    Legged_locomotion, Robot_sensing_system, Hidden_Markov_models
    Cited by 16

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    2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    Date of Conference: 9-14 Oct. 2016

    https://doi.org/10.1109/IROS.2016.7759570

     

    ■  Researchers

    Jemin HwangboCarmine Dario Bellicoso, Péter Fankhauser, Marco Hutter

    Robotic Systems Lab, ETH Zurich

     

     

    ■  Abstract

    Legged robots require a robust and fast responding feet contact detection strategy. Common force sensors are often too heavy and can be easily damaged during impacts with the terrain. Therefore, it is desirable to detect a contact without a force sensor. This paper introduces a probabilistic contact detection strategy which considers full dynamics and differential/forward kinematics to maximize the use of available information for contact estimation. This papers shows that such strategy is much more accurate than the state-of-the-art strategy that only take one measure into account, with a quadrupedal robot.

     

     

    ■  Conclusion

    This paper introduced a novel approach to identify a contact for a multibody robotic system. Unlike traditional approaches which use a state observer or heuristic methods, the proposed method uses a probabilistic approach. Consequently, it can fuse multiple sensor measurements in estimation. The measurement models shows that not any single measure can detect a contact accurately and robustly. Only the combined information provides a reliable contact state estimate. The proposed method was verified both in simulation and with a real hardware. The proposed contact detection strategy was reliable in both cases. Even though this paper describes the basic version of the approach, the proposed strategy can also include additional sensors (e.g. IMU on leg links) thanks to its probabilistic formulation.

     

     

     

     

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