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Sampling-Based MPC for Constrained Vision Based Control


Visual servoing control schemes, such as Image-Based (IBVS), Pose Based (PBVS) or Hybrid-Based (HBVS) have been extensively developed over the last decades making possible their uses in a large number of applications. It is well-known that the main problems to be handled concern the presence of local minima or singularities, the visibility constraint, the joint limits, etc. Recently, Model Predictive Path Integral (MPPI) control algorithm has been developed for autonomous robot navigation tasks. In this paper, we propose a MPPI-VS framework applied for the control of a 6-DoF robot with 2D point, 3D point, and Pose Based Visual Servoing techniques. We performed intensive simulations under various operating conditions to show the potential advantages of the proposed control framework compared to the classical schemes. The effectiveness, the robustness and the capability in coping easily with the system constraints of the control framework are shown.


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Dates and versions

lirmm-03313645 , version 1 (04-08-2021)
lirmm-03313645 , version 2 (04-10-2021)



Ihab S. Mohamed, Guillaume Allibert, Philippe Martinet. Sampling-Based MPC for Constrained Vision Based Control. IROS 2021 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep 2021, Prague, Czech Republic. ⟨10.1109/IROS51168.2021.9635970⟩. ⟨lirmm-03313645v2⟩
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