FaultBuster: data driven fault detection and diagnosis for industrial systems

B176 http://www.issi.uz.zgora.pl/ACD_2009

EKF Application on Estimating Missile Guidance Signals

An Extended Kalman Filter (EKF) application has been developed to estimate relative position, relative velocity, Line Of Sight (LOS), LOS rate and target acceleration in the nonlinear missile-target relative kinematics. The used measures come from a strapdown seeker and an IMU. In order to improve the guidance performance, the setting of the filter against different types of target maneuvers has been studied. In all considered scenarios the EKF yields a remarkable improvement of miss distance values. The used approach and the filter set-up allow to include uncertainties such as misalignments, non synchronisms between sensors or seeker measures aberrations. The introduction of a supervision to adapt the setting to different identified target manoeuvres could be developed. [DOI: 10.1685/CSC09323] About DO

an embedded database technology perspective in cyber physical production systems

Abstract The goal of the paper is the proposition of an enabling technology for the control and optimization of cyber-physical production systems, oriented to the lightweight implementation of performance metrics methodology in a network of distributed devices. The database-centric perspective, applied to distributed devices, supports the adoption of well-known key performance metrics for viable lightweight control policies and optimization of complex scenarios in the factory of the future. An experiment of the technique on a real case data set is presented and analyzed

Embedded solutions for a class of highly unstable, underactuated and self-balancing robotic systems

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Redundancy analysis of cooperative dual-arm manipulators

This paper presents the redundancy analysis of two cooperative manipulators, showing how they can be considered as a single redundant manipulator through the use of the relative Jacobian matrix. In this way, the kinematic redundancy can be resolved by applying the principal local optimization techniques used in the single manipulator case. We resolve the redundancy by using the Jacobian null space technique, which permits us to perform several tasks with different execution priority levels at the same time; this is a useful feature, especially when the manipulators are to be mounted on and cooperate with a mobile platform. As an illustrative example, we present a case study consisting of two planar manipulators mounted on a smart wheelchair, whose degrees of redundancy are employed to move an object along a pre-defined path, while avoiding an obstacle in the manipulator's workspace at the same time

Probabilistic Self-Localization and Mapping: An Asynchronous Multirate Approach

"© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] In this paper, we present a set of robust and efficient algorithms with O(N) cost for the solution of the Simultaneous Localization And Mapping (SLAM) problem of a mobile robot. First, we introduce a novel object detection method, which is mainly based on multiple line fitting method for landmark detection with regular constrained angles. Second, a line-based pose estimation method is proposed, based on LeastSquares (LS). This method performs the matching of lines, providing the global pose estimation under assumption of known Data-Association. Finally, we extend the FastSLAM (FActored Solution To SLAM) algorithm for mobile robot self-localisation and mapping by considering the asynchronous sampling of sensors and actuators. In this sense, multi-rate asynchronous holds are used to interface signals with different sampling rates. Moreover, an asynchronous fusion method to predict and update mobile robot pose and map is also presented. In addition to this, FastSLAM 1.0 has been also improved by considering the estimated pose with the LS-approach to re-allocate each particle of the posterior distribution of the robot pose. This approach has a lower computational cost than the original Extended Kalman Filtering (EKF) approach in FastSLAM 2.0. All these methods have been combined in order to perform an efficient and robust self-localization and map building process. Additionally, these methods have been validated with experimental real data, in mobile robot moving on an unknown environment for solving the SLAM problem.This work has been supported by the Spanish Government (MCyT) research project BIA2005-09377-C03-02 and by the Italian Government (MIUR) research project PRIN2005097207.Armesto, L.; Ippoliti, G.; Longhi, S.; Tornero Montserrat, J. (2008). Probabilistic Self-Localization and Mapping: An Asynchronous Multirate Approach. IEEE Robotics & Automation Magazine. 15(2):77-88. https://doi.org/10.1109/M-RA.2007.907355S778815