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    Optimal Active Sensing with Process and Measurement Noise


    Cognetti, Marco and Salaris, Paolo and Robuffo Giordano, Paolo (2018) Optimal Active Sensing with Process and Measurement Noise. 2018 IEEE International Conference on Robotics and Automation (ICRA). pp. 2118-2125. ISSN 2577-087X

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    Abstract

    The goal of this paper is to increase the estimation performance of an Extended Kalman Filter for a nonlinear differentially flat system by planning trajectories able to maximize the amount of information gathered by onboard sensors in presence of both process and measurement noises. In a previous work, we presented an online gradient descent method for planning optimal trajectories along which the smallest eigenvalue of the Observability Gramian (OG) is maximized. As the smallest eigenvalue of the OG is inversely proportional to the maximum estimation uncertainty, its maximization reduces the maximum estimation uncertainty of any estimation algorithm employed during motion. However, the OG does not consider the process noise that, instead, in several applications is far from being negligible. For this reason, this paper proposes a novel solution able to cope with non-negligible process noise: this is achieved by minimizing the largest eigenvalue of the a posteriori covariance matrix obtained by solving the Continuous Riccati Equation as a measure of the total available information. This minimization is expected to maximize the information gathered by the outputs while, at the same time, limiting as much as possible the negative effects of the process noise. We apply our method to a unicycle robot. The comparison between the novel method and the one of our previous work (which did not consider process noise) shows significant improvements in the obtained estimation accuracy.

    Item Type: Article
    Keywords: Robot sensing systems; Estimation; Trajectory; Eigenvalues and eigenfunctions; Observability;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15332
    Identification Number: https://doi.org/10.1109/ICRA.2018.8460476
    Depositing User: Marco Cognetti
    Date Deposited: 24 Jan 2022 16:44
    Journal or Publication Title: 2018 IEEE International Conference on Robotics and Automation (ICRA)
    Publisher: IEEE
    Refereed: Yes
    URI:

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