Navigation of Real Mobile Robot by Using Fuzzy Logic Technique

Now a day’s robots play an important role many applications like medical, industrial, military, transportation etc. navigation of mobile robot is the primary issue in now a days. Navigation is the process of detection and avoiding the obstacles in the path and to reach the destination by taking the surrounding information from the sensors. The successful navigation of mobile robot means reaching the destination in short distance in short period by avoiding the obstacles in the path. For this, we are using fuzzy logic technique for the navigation of mobile robot. In this project, we build up the four-wheel mobile robot and simulation and experimental results are carried out in the lab. Comparison between the simulation and experimental results are done and are found to be in good

Navigation of Mobile Robot using Fuzzy Logic

In this paper research has been carried out to develop a navigation technique for an autonomous robot to work in a real world environment, which should be capable of identifying and avoiding obstacles, specifically in a very busy a demanding environment. In this paper better technique is develop in navigating mobile robot in above mention environment. The action and reaction of the robot is addressed by fuzzy logic control system. The input fuzzy members are turn angle between the robot head and the target, distance of the obstacles present all around the robot (left, right, front, back).The above mention input members are senses by series of infrared sensors. The presented FLC for navigation of robot has been applied in all complex and adverse environment. The results are hold good for all the above mention conditions

Path planning and control of mobile robot using fuzzy logic

In this paper study has been carried out to improve a steering technique for an self-directed bot to work in a real world atmosphere, which should be proficient of classifying and evading hindrances, precisely in a very busy a challenging atmosphere. In this paper better method is develop in circumnavigating mobile bot in afore said atmosphere. The action and reaction of the bot is addressed by fuzzy logic control scheme. The input fuzzy members are turn angle between the bot head and the target, distance of the hindrances present all around the bot (lef, rgh, and front, back).The aforesaid input members are sensed by series of infrared sensors. The obtainable FLC for steering of bot has been applied in all complex and hostile atmosphere. The outcomes hold good for all the above mention situations

Fuzzy Mobile-Robot Positioning in Intelligent Spaces Using Wireless Sensor Networks

This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using Wireless Sensor Networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods

SISTEMA DE LOCALIZACIÓN POR RADAR PARA UN ROBOT DE CONFIGURACIÓN ACKERMANN (RADAR LOCATION SYSTEM FOR AN ACKERMANN CONFIGURATION ROBOT)

Resumen El proyecto consiste en desarrollar un sistema de localización capaz de llevar a cabo el control de la trayectoria de un robot móvil, este robot está diseñado bajo los parámetros de la configuración de movimiento Ackermann y fue construido con un chasis de acrílico de 3 mm en el que se montaron los componentes que lo conforman, los cuales son: el sistema de dirección, sistema de tracción y el sistema de control. Además, se recurrió a la tecnología de impresión 3D para fabricar una base necesaria para evitar interferencias en el escáner láser. Para la elaboración del sistema de localización se requiere el uso del escáner láser RPLiDAR modelo A1M8, que actúa como el principal componente para obtener una retroalimentación del ambiente. Esto se traduce como el uso de los datos que entrega el escáner, los cuales mediante análisis matemáticos ayudan al establecimiento de un sistema coordenado y el cálculo de la posición. La conexión del sistema de localización y el sistema de control brinda la posibilidad de programar las trayectorias a seguir por el robot, sin embargo, debido a la existencia de errores se implementa un sistema de recálculo de trayectoria que toma como principales herramientas de funcionamiento la cinemática del robot y el filtro de partículas creado para el cálculo de incertidumbre. Este recálculo reduce considerablemente el error en la trayectoria, lo cual queda demostrado en las pruebas realizadas en una trayectoria compuesta por: 3 movimientos rectos de 40 cm de longitud y 2 movimientos curvos con un radio de curvatura de 40 cm. La precisión en cuanto al cumplimiento de la trayectoria del sistema de recálculo es alta, ya que se observan variaciones por debajo de los 10 cm, comparado con un comportamiento ideal. Palabras clave: configuración Ackermann, escáner láser, robot móvil, incertidumbre, sistemas de radar, cinemática, filtro de partículas. Abstract The project consists of developing a location system capable of controlling the trajectory of a mobile robot, this robot is designed under the parameters of the Ackermann movement configuration and was built with a 3 mm acrylic chassis in the that the components that comprise it were assembled, which are: the steering system, traction system and the control system; In addition, 3D printing technology was used to manufacture a necessary base to avoid interference in the laser scanner. For the development of the location system, the use of the RPLiDAR model A1M8 laser scanner is required, which acts as the main component to obtain feedback from the environment in which the robot moves, this is translated as the use of the data provided by the scanner, which through mathematical analysis help the establishment of a coordinate system and the calculation of the position. The connection of the location system and the control system offers the possibility of programming the trajectories to be followed by the robot, however, due to the existence of errors, a trajectory recalculation system is implemented that takes the kinematics as its main operating tools. of the robot and the particle filter created for the uncertainty calculation; This recalculation considerably reduces the error in the trajectory, which is demonstrated in the tests carried out on a trajectory composed of 3 straight movements of 40 cm in length and 2 curved movements with a radius of curvature of 40 cm. The precision in terms of compliance with the trajectory of the recalculation system is high, since variations are observed below 10 cm compared to an ideal behavior. Keywords: Ackermann configuration, laser scanner, mobile robot, uncertainty, radar systems, kinematics, particle filter

A real-time data mining technique applied for critical ECG rhythm on handheld device

Sudden cardiac arrest is often caused by ventricular arrhythmias and these episodes can lead to death for patients with chronic heart disease. Hence, detection of such arrhythmia is crucial in mobile ECG monitoring. In this research, a systematic study is carried out to investigate the possible limitations that are preventing the realisation of a real-time ECG arrhythmia data-mining algorithm suitable for application on mobile devices. Based on the findings, a computationally lightweight algorithm is devised and tested. Ventricular tachycardia (VT) is the most common type of ventricular arrhythmias and is also the deadliest.. A ventricular tachycardia (VT) episode is due to a disorder ofthe regular contractions ofthe heart. It occurs when the human heart ventricles generate a rapid heartbeat which disrupts the regular physiology cycle. The normal sinus rhythm (NSR) of a regular human heart beat signal has its signature PQRST waveform and in regular pattern. Whereas, the characteristics of a ventricular tachycardia (VT) signal waveforms are short R-R intervals, widen QRS duration and the absence of P-waves. Each type of ECG arrhythmia previously mentioned has a unique waveform signature that can be exploited as features to be used for the realization of an automated ECG analysis application. In order to extract this known ECG waveform feature, a time-domain analysis is proposed for feature extraction. Cross-correlation allows the computation of a co-efficient that quantifies the similarity between two times-series. Hence, by cross-correlating known ECG waveform templates with an unknown ECG signal, the coefficient can indicate the similarities. In previous published work, a preliminary study was carried out. The cross-correlation coefficient wave (CCW) technique was introduced for feature extraction. The outcome ofthis work presents CCW as a promising feature to differentiate between NSR, VT and Vfib signals. Moreover, cross-correlation computation does not require high computational overhead. Next, an automated detection algorithm requires a classification mechanism to make sense of the feature extracted. A further study is conducted and published, a fuzzy set k-NN classifier was introduced for the classification of CCW feature extracted from ECG signal segments. A training set of size 180 is used. The outcome of the study indicates that the computationally light-weight fuzzy k-NN classifier can reliably classify between NSR and VT signals, the class detection rate is low for classifying Vfib signal using the fuzzy k-NN classifier. Hence, a modified algorithm known as fuzzy hybrid classifier is proposed. By implementing an expert knowledge based fuzzy inference system for classification of ECG signal; the Vfib signal detection rate was improved. The comparison outcome was that the hybrid fuzzy classifier is able to achieve 91.1% correct rate, 100% sensitivity and 100% specificity. The previously mentioned result outperforms the compared classifiers. The proposed detection and classification algorithm is able to achieve high accuracy in analysing ECG signal feature of NSR, VT and Vfib nature. Moreover, the proposed classifier is successfully implemented on a smart mobile device and it is able to perform data-mining of the ECG signal with satisfiable results

A fuzzy logic approach to localisation in wireless local area networks

This thesis examines the use and value of fuzzy sets, fuzzy logic and fuzzy inference in wireless positioning systems and solutions. Various fuzzy-related techniques and methodologies are reviewed and investigated, including a comprehensive review of fuzzy-based positioning and localisation systems. The thesis is aimed at the development of a novel positioning technique which enhances well-known multi-nearest-neighbour (kNN) and fingerprinting algorithms with received signal strength (RSS) measurements. A fuzzy inference system is put forward for the generation of weightings for selected nearest-neighbours and the elimination of outliers. In this study, Monte Carlo simulations of a proposed multivariable fuzzy localisation (MVFL) system showed a significant improvement in the root mean square error (RMSE) in position estimation, compared with well-known localisation algorithms. The simulation outcomes were confirmed empirically in laboratory tests under various scenarios. The proposed technique uses available indoor wireless local area network (WLAN) infrastructure and requires no additional hardware or modification to the network, nor any active user participation. The thesis aims to benefit practitioners and academic researchers of system positioning

Intelligent Navigational Strategies For Multiple Wheeled Mobile Robots Using Artificial Hybrid Methodologies

At present time, the application of mobile robot is commonly seen in every fields of science and engineering. The application is not only limited to industries but also in thehousehold, medical, defense, transportation, space and much more. They can perform all kind of tasks which human being cannot do efficiently and accurately such as working in hazardous and highly risk condition, space research etc. Hence, the autonomous navigation of mobile robot is the highly discussed topic of today in an uncertain environment. The present work concentrates on the implementation of the Artificial Intelligence approaches for the mobile robot navigation in an uncertain environment. The obstacle avoidance and optimal path planning is the key issue in autonomous navigation, which is solved in the present work by using artificial intelligent approaches. The methods use for the navigational accuracy and efficiency are Firefly Algorithm (FA), Probability- Fuzzy Logic (PFL), Matrix based Genetic Algorithm (MGA) and Hybrid controller (FAPFL,FA-MGA, FA-PFL-MGA).The proposed work provides an effective navigation of single and multiple mobile robots in both static and dynamic environment. The simulational analysis is carried over the Matlab software and then it is implemented on amobile robot for real-time navigation analysis. During the analysis of the proposed controller, it has been noticed that the Firefly Algorithm performs well as compared to fuzzy and genetic algorithm controller. It also plays an important role inbuilding the successful Hybrid approaches such as FA-PFL, FA-MGA, FA-PFL-MGA. The proposed hybrid methodology perform well over the individual controller especially for pathoptimality and navigational time. The developed controller also proves to be efficient when they are compared with other navigational controller such as Neural Network, Ant Colony Algorithm, Particle Swarm Optimization, Neuro-Fuzzy etc