Experimental Validation of Sensitivity-Aware Trajectory Planning for a Redundant Robotic Manipulator Under Payload Uncertainty
Ali Srour, Antonio Franchi, Paolo Robuffo Giordano, Marco Cognetti
Abstract
In this letter, we experimentally validate the recent concepts of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop state and input sensitivity</i> in the context of robust manipulation control for a robot manipulator. Our objective is to assess how optimizing trajectories with respect to sensitivity metrics can enhance the closed-loop system's performance w.r.t. model uncertainties, such as those arising from payload variations during precise manipulation tasks. We conduct a series of experiments to validate our optimization approach across different trajectories, focusing primarily on evaluating the precision of the manipulator's end-effector at critical moments where high accuracy is essential. Our findings offer valuable insights into improving the closed-loop robustness of the robot's state and inputs against physical parametric uncertainties that could otherwise degrade the system's performance.
BibTeX
@inproceedings{ral2025_experimentalvali,
title = {Experimental Validation of Sensitivity-Aware Trajectory Planning for a Redundant Robotic Manipulator Under Payload Uncertainty},
author = {Ali Srour and Antonio Franchi and Paolo Robuffo Giordano and Marco Cognetti},
booktitle = {RA-L 2025},
year = {2025}
}