Sensor fusion-based localization methods for mobile robots : a case study for wheeled robots

Localization aims to provide the best estimate of the robot pose. It is a crucial algorithm in every robotics application, since its output directly determines the inputs of the robot to be controlled in its configuration space. In real world of engineering, the robot dynamics related measurements are subject to both uncertainties and disturbances. These error sources yield unreliable inferences of the robot state, which inherently result in wrong consensus about the appropriate control strategy to be applied. This outcome may drive the system out of stability and damage both the physical system and its environment. The localization algorithm captures the uncertainties with probabilistic approaches. Namely, the measurement processes are modelled along with their unreliability, moreover, the synergy of multiple information sources is formulated with the aim to calculate the most probable estimate of the robot pose. In essence, this algorithm is composed of two main parts, i.e., first the dynamics of the system is derived, and the corresponding uncertainties are initially predicted, next the additional sensor information is incorporated in the algorithm to refine the posterior estimate. This approach provides the state-of-the-art solution for the derivation of mobile robot poses in real applications

Sensor fusion-based localization methods for mobile robots: A case study for wheeled robots

Localization aims to provide the best estimate of the robot pose. It is a crucial algorithm in every robotics application, since its output directly determines the inputs of the robot to be controlled in its configuration space. In real world of engineering, the robot dynamics related measurements are subject to both uncertainties and disturbances. These error sources yield unreliable inferences of the robot state, which inherently result in wrong consensus about the appropriate control strategy to be applied. This outcome may drive the system out of stability and damage both the physical system and its environment. The localization algorithm captures the uncertainties with probabilistic approaches. Namely, the measurement processes are modelled along with their unreliability, moreover, the synergy of multiple information sources is formulated with the aim to calculate the most probable estimate of the robot pose. In essence, this algorithm is composed of two main parts, i.e., first the dynamics of the system is derived, and the corresponding uncertainties are initially predicted, next the additional sensor information is incorporated in the algorithm to refine the posterior estimate. This approach provides the state-of-the-art solution for the derivation of mobile robot poses in real applications

A multi-chanel electrical impedance meter based on digital lock-in technology

Abstract The presented multichannel measuring system working on various frequencies is suitable either for electrical impedance spectroscopy or tomography. The authors of this paper have developed the complete measurement system and a graphical user interface platform. The accuracy of impedance amplitude and phase are 1 ppm and 0.01°, respectively. The basic instrument works with 8 channels and can be expanded to 64 channels with the application of multiplexing or multiple parallel connected instruments in the same system

Fingerprinting-Based Indoor Positioning Using Data Fusion of Different Radiocommunication-Based Technologies

Wireless-radio-communication-based devices are used in more and more places with the spread of Industry 4.0. Localization plays a crucial part in many of these applications. In this paper, a novel radiocommunication-based indoor positioning method is proposed, which applies the fusion of fingerprints extracted with various technologies to improve the overall efficiency. The aim of the research is to apply the differences, which occur due to that different technologies behave differently in an indoor space. The proposed method was validated using training and test data collected in a laboratory. Four different technologies, namely WiFi received signal strength indication (RSSI), ultra-wideband (UWB) RSSI, UWB time of flight (TOF) and RSSI in 433 MHz frequency band and all of their possible combinations, were tested to examine the performance of the proposed method. Three widely used fingerprinting algorithms, the weighted k-nearest neighbor, the random forest, and the artificial neural network were implemented to evaluate their efficiency with the proposed method. The achieved results show that the accuracy of the localization can be improved by combining different technologies. The combination of the two low-cost technologies, i.e., the WiFi and the 433 MHz technology, resulted in an 11% improvement compared to the more accurate technology, i.e., the 433 MHz technology. Combining the UWB module with other technologies results in a less significant improvement since this sensor provides lower error rates, when used alone

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints

Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints

Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data

Real-Time Vehicle Classification System Using a Single Magnetometer

Vehicle count and classification data are very important inputs for intelligent transportation systems (ITS). Magnetic sensor-based technology provides a very promising solution for the measurement of different traffic parameters. In this work, a novel, real-time vehicle detection and classification system is presented using a single magnetometer. The detection, feature extraction, and classification are performed online, so there is no need for external equipment to conduct the necessary computation. Data acquisition was performed in a real environment using a unit installed into the surface of the pavement. A very large number of samples were collected containing measurements of various vehicle classes, which were applied for the training and the validation of the proposed algorithm. To explore the capabilities of magnetometers, nine defined vehicle classes were applied, which is much higher than in relevant methods. The classification is performed using three-layer feedforward artificial neural networks (ANN). Only time-domain analysis was performed on the waveforms using multiple novel feature extraction approaches. The applied time-domain features require low computation and memory resources, which enables easier implementation and real-time operation. Various combinations of used sensor axes were also examined to reduce the size of the classifier and to increase efficiency. The effect of the detection length, which is a widely used feature, but also speed-dependent, on the proposed system was also investigated to explore the suitability of the applied feature set. The results show that the highest achieved classification efficiencies on unknown samples are 74.67% with, and 73.73% without applying the detection length in the feature set

Teaching spread spectrum in the course Telecommunication Systems using Octave

Spread spectrum is a technique used for transmitting telecommunications signals. This topic is relatively complex and can be challenging for students. Thus, programming language Octave has been introduced in the course curriculum of Telecommunication Systems as one of the powerful tools that aid in student understanding of the spread-spectrum principles. The purpose of this paper is to elaborate on the teaching method of spread-spectrum technique and its application in telecommunications, with the aim to make this topic understandable to students. The participants of the experiment were fifth-semester electrical engineering students (N = 86) at a college of applied sciences. To test the previous knowledge of students needed to understand the theory of spread spectrum, a pretest was carried out which refers to general terms from the fundamentals of telecommunications. With the aim to deeper apprehend the necessary theory from this area, the students had to solve examples from engineering practice using open source software Octave, as well as computational tasks, regarding both methods in spread-spectrum technology. It has been shown that thanks to the visualization of appropriate material, students of the experimental group understood in more detail the method of generating spread-spectrum signals and the influence of individual parameters on partial signals, in relation to students who did not have the opportunity to use Octave. The results of the research lead us to conclude that software like Octave should be included in courses like Telecommunication Systems as a supplement to the traditional teaching and learning methods

Flow Regulation of Low Head Hydraulic Propeller Turbines by Means of Variable Rotational Speed: Aerodynamic Motivations

To date, hydraulic energy is still, among the renewable ones, the most widespread and most exploited to produce electricity. With the current trend to exploit any renewable source available, the limits for the economic convenience of hydroelectric power plants have significantly changed, making it interesting and convenient to use even small heads and low flow rates. In the specific applications of hydraulic turbines operating with low heads, the Kaplan turbine plays the predominant role among the available machines, also given the possibility of carrying out an “on cam” regulation, acting simultaneously on the geometry of the rotor and distributor rows, thus allowing a wide flow rate adjustment range. However, for applications characterized by very low heads and low available powers, it may not be convenient to use complex regulating devices. For this reason, these plants usually use axial machines characterized by a partial regulation (of the distributor or of the rotor), significantly reducing the operating range of the machine compared to the case of double regulation. In the last decade, the development of reliable and less expensive permanent magnet generators and power electronic converters and related new control strategies has paved the way for the concept of regulating hydraulic turbines by means of variable rotational speed. This regulation principle is based on the possibility of acting in the case of using synchronous permanent magnets electric generators and electronic power converters and on the variation of the rotational speed of the machine while keeping the grid frequency constant. The concept can be applied both to pure propellers with fixed a rotor and fixed distributor and to hydraulic axial turbines with regulation based on the modification of the variable guide vane opening angle. Although this new regulation approach, even in the case of the combined guide vane and rotational speed regulation, does not allow to recover most of the energy losses due to the variation of the operating conditions as effectively as the Kaplan double regulation does, the variation of the rotation speed, coupled with the variation of the opening of the distributor row, allows to reduce the tangential kinetic energy losses generated at the turbine exit during the off-design operations of a fixed blade opening angle rotor. At the same time, this type of regulation offers a simple and thus low-cost solution. The present study develops the theory underlying this regulation concept, based on the use of the turbomachinery fundamental equations, and reports the results of the off-design CFD analysis carried out for different combinations of rotation speeds and openings of the distributor, showing the improvement of the hydraulic efficiency over a large range of operating conditions with respect to the single regulation approach