Mohammed Algabri

Comparative study of soft computing techniques for mobile robot navigation in an unknown environment

Robot navigation and obstacle avoidance using fuzzy logic controller is presented.Soft computing techniques are used to optimize the performance of fuzzy logic.The automatic tuning was done by using three soft computing techniques: GA, PSO, and NN.The best performance in terms of travelling time and speed is based on GA-Fuzzy.The PSO-Fuzzy and Neuro-Fuzzy methods have better performance in terms of distance travelled. An autonomous mobile robot operating in an unstructured environment must be able to deal with dynamic changes of the environment. Navigation and control of a mobile robot in an unstructured environment are one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, fuzzy logic controller is optimized by integrating fuzzy logic with other soft computing techniques like genetic algorithm, neural networks, and Particle Swarm Optimization (PSO). Soft computing techniques are used in this work to tune the membership function parameters of fuzzy logic controller to improve the navigation performance. Four methods have been designed and implemented: manually constructed fuzzy logic (M-Fuzzy), fuzzy logic with genetic algorithm (GA-Fuzzy), fuzzy logic with neural network (Neuro-Fuzzy), and fuzzy logic with PSO (PSO-Fuzzy). The performances of these approaches are compared through computer simulations and experiment number of scenarios using Khepera III mobile robot platform. Hybrid fuzzy logic controls with soft computing techniques are found to be most efficient for mobile robot navigation. The GA-Fuzzy technique is found to perform better than the other techniques in most of the test scenarios in terms of travelling time and average speed. The performances of both PSO-Fuzzy and Neuro-Fuzzy are found to be better than the other methods in terms of distance travelled. In terms of bending energy, the PSO-Fuzzy and Neuro-Fuzzy are found to be better in simulation results. Although, the M-Fuzzy is found to be better using real experimental results. Hence, the most important system parameter will dictate which of the four methods to use.

Mobile Robot Navigation and Obstacle-avoidance using ANFIS in Unknown Environment

Navigation and obstacle avoidance in an unknown environment is proposed in this paper using hybrid neural network with fuzzy logic controller. The overall system is termed as Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS combines the benefits of fuzzy logic and neural networks for the purpose of achieving robotic navigation task. Simulation results are presented using Khepera Simulator (KiKs) within MATLAB environment. Moreover, experimental results are obtained using Khepera III platform.

Visual Tracking in Unknown Environments Using Fuzzy Logic and Dead Reckoning

This paper addresses the problem of making a non-holonomic wheeled mobile robot (WMR) move to a target object using computer vision and obstacle-avoidance techniques. If a priori information about the obstacles is available, pre-planning the desired path can be a good candidate method. However, in so many cases, obstacles are dynamic. Therefore, our first challenge is to make the WMR move to a desired target while autonomously avoiding any obstacle along its path. The second challenge deals with visual-tracking loss; that is, when the target is lost from the camera scope, the robot should use Dead Reckoning (DR) to get back on its path towards the target. The Visual Tracking (VT) algorithm then takes the relay to reach the final destination, compensating for any errors due to DR by calculating the distance to the target when it is within the scope of the camera. The proposed system also uses two fuzzy-logic controllers; the first controller avoids objects while the second manages the path to the target. Different complex scenarios have been implemented, showing the validity of our multi-controller model.

An autonomous stereovision-based navigation system (ASNS) for mobile robots

Recently, stereovision has appeared in robotics as a source of information for real-time mapping and path planning. In this paper, an intelligent motion system for mobile robots is designed and implemented using stereovision. The proposed system uses stereovision as a primary method for sensing the environment, and the system is able to navigate intelligently in an indoor environment with varying degrees of obstacle complexity. It creates noiseless and high-confidence 3D point clouds and uses these point clouds as an input for the mapping and path-planning modules. The proposed system was built by developing, enhancing, and integrating various techniques, modules and algorithms. The Stereovision-based Path-planning module is the integration of three main enhanced techniques: (1) the multi-baseline multi-view stereovision filter (MMSVF), (2) accurate floor detection and segmentation (AFDS), and (3) the intelligent gazing module (IGM). This Stereovision-based Path planning (MMSVF, IGM, and AFDS) was integrated with the Fuzzy Logic Motion Controller (FLMC). All techniques, modules and algorithms are implemented using a multi-threaded and client–server-based architecture. To prove the viability and robustness of our proposed system, we have integrated all components of the system into a fully functional mobile robot navigation system. We compared the performance of the main modules with that of similar modules in the literatures, and showed that our modules had better performance. Testing the whole system is more important than just testing each module individually. To the best of our knowledge, the literatures lack such testing. Hence, in this paper we present the performance of our complete integrated system in different environments using different parameters and different architectures.

Mobile robot nonlinear feedback control based on Elman neural network observer

This article presents a new approach to control a wheeled mobile robot without velocity measurement. The controller developed is based on kinematic model as well as dynamics model to take into account parameters of dynamics. These parameters related to dynamic equations are identified using a proposed methodology. Input–output feedback linearization is considered with a slight modification in the mathematical expressions to implement the dynamic controller and analyze the nonlinear internal behavior. The developed controllers require sensors to obtain the states needed for the closed-loop system. However, some states may not be available due to the absence of the sensors because of the cost, the weight limitation, reliability, induction of errors, failure, and so on. Particularly, for the velocity measurements, the required accuracy may not be achieved in practical applications due to the existence of significant errors induced by stochastic or cyclical noise. In this article, Elman neural network is proposed to work as an observer to estimate the velocity needed to complete the full state required for the closed-loop control and account for all the disturbances and model parameter uncertainties. Different simulations are carried out to demonstrate the feasibility of the approach in tracking different reference trajectories in comparison with other paradigms.

Optimization of soft cost estimation using genetic algorithm for NASA software projects

Software cost estimation approximate judgment of the cost and time required to complete the project successfully. The cost estimation is usually measured in terms of effort. The Constructive Cost Model (COCOMO) is one of the most important model for software cost estimation. In this paper, soft computing techniques: genetic algorithm has been used for tuning the parameters of COCOMO model to predicate the software cost more accurately. Moreover, the performance of proposed methods compared through COCOMO NASA Data Set were used.

Soft Computing Techniques for Classification of Voiced/Unvoiced Phonemes

A method that uses fuzzy logic to classify two simple speech features for the automatic classification of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computin...

Automatic Speaker Recognition for Mobile Forensic Applications

Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. In general, speaker recognition is used for discriminating people based on their voices. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel. The identification of a person through his voice within a forensic quality context is challenging. In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results. The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel. This diversity facilitates its usage in forensic experimentations. Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling. Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.

Wireless vision-based fuzzy controllers for moving object tracking using a quadcopter

During the last few decades, the utilization of unmanned aerial vehicles has grown in military and has seen new civil applications because of their reduced cost and hovering capabilities. This article presents a visual servoing system for detecting and tracking a moving object using an unmanned aerial vehicle. The system consists of two sequential components. The first component addresses the detection of a moving object, using the unmanned aerial vehicle based on color features. The second component addresses the visual servoing control that is designed to guide the unmanned aerial vehicle according to the target position and inclination. Three fuzzy logic modules are implemented in order to control the unmanned aerial vehicle in tracking a moving object. The proposed method is validated on a real flight using an AR.Drone 2.0 quadcopter. The obtained results show that the performance of the proposed method is suitable for object-following tasks in surveillance applications. This research investigates the use of unmanned aerial vehicle technology for crowd monitoring during Hajj and it could also be used for border surveillance to monitor Saudi Arabia’s borders.

Optimization of Fuzzy Logic Controller Using PSO for Mobile Robot Navigation in an Unknown Environment

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.