Pietro Muraca

An adaptive quasi-notch filter for a biased sinusoidal signal estimation

This note presents an alternative approach to classical adaptive notch filters designed to identify the frequency, magnitude, phase and offset of a biased sinusoidal signal. The algorithm takes advantages of an adaptive law for the resonant frequency of a third-order generalized integrator as a part of an orthogonal-signals generator system. The resulting estimator presents a dynamic order equal to 4. The strength of the discussed strategy results in a fast and accurate signal tracking capability and a good rejection to noise. The properties of the algorithm are verified in simulations in a range of signal conditions, such as step and sweep changes in frequency and voltage sag confirming the effectiveness of the strategy for estimation and tracking of time-varying parameters.

An Extended Kalman Filter for the state estimation of a mobile robot from intermittent measurements

Position and orientation estimation is one of the main problem in mobile robotics: to navigate and accomplish its job a mobile robot must know where is it. To this sake, we suppose that a set of wireless sensors are located on the operation field, which can measure distance and orientation of the robot, and are connected with the estimator by a wireless network. An extended Kalman filter has been build up to reconstruct the state of the robot, which into account that the information from the sensors may be lost. Also, in order to save the lifetime of the batteries of the sensors we suppose that the sensors normally sleep (low consumption of energy) and send the information (hight consumption of energy) only if the robot activates them. Therefore, a strategy of scanning of the sensors has been defined, to select at each time which sensors to activate without degrading too much the quality of the estimate.

Mobile robot localization via EKF and UKF

In this work we compare the performance of two well known filters for nonlinear models, the Extended Kalman Filter and the Unscented Kalman Filter, in estimating the position and orientation of a mobile robot. The two filters fuse the measurements taken by ultrasonic sensors located onboard the robot. The experimental results on real data show a substantial equivalence of the two filters, although in principle the approximating properties of the UKF are much better. A switching sensors activation policy is also devised, which allows to obtain an accurate estimate of the robot state using only a fraction of the available sensors, with a relevant saving of battery power. Analyzes using EKF and UKF to fuse measurements from ultrasonic sensors in robotics.Shows that the EKF performs as good as the UKF for mobile robot localization.Proposes a sensor switching rule to use only a fraction of the available sensors.Data comes from a real laboratory setting.

On the optimal design of the output transformation for discrete-time linear systems

The optimization of the output matrix for a discrete-time, single-output, linear stochastic system is approached from two different points of view. Firstly, we investigate the problem of minimizing the steady-state filter error variance with respect to a time-invariant output matrix subject to a norm constraint. Secondly, we propose a filter algorithm in which the output matrix at timek is chosen so as to maximize the difference at timek+1 between the variance of the prediction error and that of the a posteriori error. For this filter, boundedness of the covariance and asymptotic stability are investigated. Several numerical experiments are reported: they give information about the limiting behavior of the sequence of output matrices generated by the algorithm and the corresponding error covariance. They also enable us to make a comparison with the results obtained by solving the former problem.

Extended and Unscented Kalman Filters for mobile robot localization and environment reconstruction

In this work we compare the performance of two algorithms, respectively based on the Extended Kalman Filter and the Unscented Kalman Filter, for the mobile robot localization and environment reconstruction problem. The proposed algorithms do not require any assumption on the robot working space: they are driven only by the measurements taken using ultrasonic sensors located onboard the robot. We also devise a switching sensors activation policy, which allows energy saving still achieving accurate tracking and reliable mapping of the workspace. The results show that the two filters work comparably well, in spite of the superior theoretical properties of the Unscented Filter.

A variable-structure regulator for robotic systems

Abstract This paper proposes a variable-structure regulator for robotic systems. It is based on the concept of sliding sectors, introduced by Shyu, Tsai and Yung for linear systems, which overcomes the chattering problem typical of the sliding-modes approach. The stability of the controlled system is proved; a numerical simulation is enclosed as an example.

An adaptive “quasi” repetitive controller for the fundamental frequency estimation of periodic signals

Abstract A repetitive control scheme for asymptotic tracking of the fundamental frequency of periodic signals is presented. The method uses an adaptive orthogonal signals generator based on a second order generalized integrator for identifying the fundamental frequency of the signal with accuracy depending on the adaptive gain. The robustness of the repetitive controller is established by investigating its sensitivities with respect to an inaccurate estimation of the period.

A polynomial based SLAM algorithm for mobile robots using ultrasonic sensors - Experimental results

In this work the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot placed in an unknown indoor environment is faced. The environment is modeled as a set of polynomials used as SLAM landmarks. A polynomial based mapping algorithm is proposed and used along with an Extended Kalman filter to yield a solution to the SLAM problem. The filter updates the robot position and orientation estimation and the environment mapping using sparse measurements taken from a set of on board ultrasonic sensors. The proposed algorithm has been evaluated in both numerical and experimental tests obtaining very good estimation and mapping results.

An obstacle avoidance receding horizon control scheme for autonomous vehicles

The paper addresses the obstacle avoidance motion planning problem for ground vehicles operating in uncertain environments. By resorting to set-theoretic ideas, a receding horizon control algorithm is proposed for robots modelled by linear time-invariant (LTI) systems subject to input and state constraints and disturbance effects. Sequences of inner ellipsoidal approximations of the exact one-step controllable sets are pre-computed for all the possible obstacle scenarios and then on-line exploited to determine the more adequate control action to be applied to the robot in a receding horizon fashion. The resulting framework guarantees Uniformly Ultimate Boundedness and constraints fulfilment regardless of any obstacle scenario occurrence.

Filters for mobile robots: EKF, UKF and sensor switching - experimental results

In this work we compare the performance of two well known filters for nonlinear models, the Extended Kalman Filter and the Unscented Kalman Filter, in estimating the position and orientation of a mobile robot. The two filters are driven by measurements taken by ultrasonic sensors that are located onboard the robot, and a switching sensors activation policy is devised, which allows power saving and accurate tracking. The experimental results show a substantial equivalence of the two filters, although in principle the approximating properties of the UKF are much better.