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Output-sampled model predictive path integral control (oMPPI) for precision tracking in active vision systems
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DOI：10.1016/j.mechatronics.2026.103557.png)
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Abstract
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Although sample-based model predictive control (MPC), such as model predictive path integral control (MPPI), are well suited to manage complex tasks like tracking a teleoperated robot while maintaining constraints and avoiding obstacles, it can be challenging to design the MPPI input sampling to achieve precision tracking. The main contribution of this work is to enable precision tracking with MPPI-type sampling methods by (i) sampling the system’s reference outputs and (ii) using inversion based control to correct for system dynamics. An advantage of the proposed reference-output-sampled MPPI (oMPPI) is that the selected reference output’s sample distribution can reflect the desired output of the system, such as the trajectory of the teleoperated robot in the active vision application. The proposed oMPPI is applied to a crane-robot active vision system for confined space inspection during aircraft wing manufacturing, enabling a teleoperated manipulator to navigate around in-wing structures. Teleoperation experiments show that oMPPI sampling increases tracking precision of a teleoperated manipulator by 22% and reduces camera oscillations by 65% when compared to MPPI sampling without inversion.
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