Abstract This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.

    Highlights Proposed a real-time AEKF to estimate pose and motion of non-cooperative target. Enhanced visual servo for autonomous robotic capture of dynamic target by AEKF. Validated experimentally the approach by a robotic manipulator with eye-in-hand camera.


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    Titel :

    Autonomous robotic capture of non-cooperative target by adaptive extended Kalman filter based visual servo


    Beteiligte:
    Dong, Gangqi (Autor:in) / Zhu, Zheng H. (Autor:in)

    Erschienen in:

    Acta Astronautica ; 122 ; 209-218


    Erscheinungsdatum :

    2016-02-05


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch