Abstract—Mobile robots are applied everywhere in the human’s life, starting from industries to domestics. This phenomenon makes it one of the most studied subjects in electronics engineering. Navigation is always an issue for this kind of robot, to ensure it can finish its task safely. Giving it a ”brain” is one of the ways to create an autonomous navigating robot. The Fuzzylogiccontroller is a good choice for the ”brain” since it does not need accurate mathematical modeling of the system. Only by utilizing the inputs from sensors are enough to design an effective controller. This paper presents an FLC design for leader-followerrobot. This FLC design is the improvement of FLC application in a single two differential-driven mobile robot. The relation between leader and followerrobot is modeled linearly as a spring-damper system. Simulation proves the feasibility of the proposed method in several environment setting, and this paper also shows that the method can be easily extended to one leader and more than one follower’s formation. The research in this paper has introduced in the classroom as the teaching-learning media to improve students’ involvement and interest in robotics and robotics related class. This paper is also part of our campaign and encouragement for teachers and students to use low-cost and open source software since not all the universities in developing country can afford the expensive high-end software.
for the navigation of mobile robot. The parameters defining the fuzzy implications are identified by genetic algorithm hybrid scheme to minimize mean square errors globally. A genetic-fuzzy approach for mobile robotnavigation among moving obstacles is presented in . The proposed approach optimizes the travel time of a robot off-line by simultaneously finding an optimal fuzzy rule base and optimal scaling factors of the static variables. In , A fuzzy system is designed to control omni-directional mobile robot based on genetic algorithms so that it can move to any direction and spin at a rotating rate. The language of VHDL is used to design the selected fuzzy system structure and it is realized on a FPGA chip to control the robot. The automated design of a fuzzycontroller using genetic algorithms for the implementation of wall following behaviors in mobile robot is presented in . The algorithm is based on the Iterative Rule Learning (IRL) approach and parameter (δ) is defined for selecting the relation between the number of rules and the quality and accuracy of controller. In , a novel method of integrated fuzzylogic and genetic algorithm for solving simultaneous localization and mapping problem of mobile robot is presented. It is based on island genetic algorithm (IGA) which search for most probable maps such that the associated poses provides the robot with the best localization information. In , a tracking controller for dynamic mobile robot by integrating a kinematic and torque controller based on type-2 fuzzylogic and genetic algorithm is presented. Genetic algorithms are used to optimize the constants for trajectory controller and parameters for membership function for fuzzylogic. In , the design and optimization of structural fuzzycontroller for mobile robot obstacle avoidance is presented. A special tailored genetic algorithm is used
Several technique have proposed to give a solution with good performance , , , , , , , , , , . However, in the robotic applica- tion, there are some uncertainties in the system, due to the sensors imprecision, inac- curate actuator and environment change every time . In terms of robot’s position and orientation, it produces the accumulation error of robot formation. The fuzzylogic control algorithm can overcome the uncertainty problems . Unfortunately, the type-1 fuzzylogiccontroller (T1FLC) can’t ensure the performance, because the uncertainty is crisp value. To the best our knowledge, only a few of existing results have been presented to solve the problem of leader-follower formation control based on the interval type-2 fuzzylogiccontroller (IT2FLC) , , . This paper aims to investigate how to design the leader-follower formation control based on the IT2FLC for achieving robust formation against the leader faults. The rest of paper is organized as follows: Section 2 briefly discusses the leader-follower kinematic model while Section 3 describes our proposed controller and material. To demonstrate the usefulness of the proposed control algorithm, the simulations and the result are pre- sented in Section 4, and finally, the conclusion of the study is given in Section 5.
Modelling and Fuzzy Control of a Four-Wheeled Mobile Robot by Istvan et al.  in this, paper they compare the results of PID controller and fuzzy controllers. For this, they have been upgraded the simplified kinematic bicycle model of a four-wheeled robot car . From these results, the fuzzycontroller shows good results in terms of speed, and it is because its behaviour is closer to reality, although fuzzycontroller required more calculations than PID controller. Adaptive Dynamic Motion ControllerDesign for a Four-Wheeled Omnidirectional Mobile Robot by Ching-Chih Tsai et al.  developed a dynamic model and a dynamic motion controller for stabilization and trajectory tracking of an omnidirectional mobile robot with four independent driving omnidirectional wheels equally spaced at 90 degrees from one to another.
This paper presents the design of an autonomous robot as a basic development of an intelligent wheeled mobile robot for air duct or corridor cleaning. The robotnavigation is based on wall following algorithm. The robot is controlled us- ing fuzzy incremental controller (FIC) and embedded in PIC18F4550 microcontroller. FIC guides the robot to move along a wall in a desired direction by maintaining a constant distance to the wall. Two ultrasonic sensors are installed in the left side of the robot to sense the wall distance. The signals from these sensors are fed to FIC that then used to de- termine the speed control of two DC motors. The robot movement is obtained through differentiating the speed of these two motors. The experimental results show that FIC is successfully controlling the robot to follow the wall as a guid- ance line and has good performance compare with PID controller.
In real-world problem for autonomous mobile robotnavigation, it should be capable of sensing its environment, understanding the sensed information to receive the knowledge of its location and surrounding environment, planning a real-time path from a starting position to goal position with hurdle avoidance, and controlling the robot steering angle and its speed to reach the target. FuzzyLogic is used in the design of possible solutions to perform local navigation, global navigation, path planning, steering control and speed control of a mobile robot. FuzzyLogic (FL) and Artificial Neural Network (ANN) are used to assist autonomous mobile robot move, learn the environment and reach the desired target .
avoidance and go to predefined position can be subdivided into simple tasks which are easier to manage. This divide-and-conquer approach has been deployed in their work and proved to be a successful approach for it makes the system modular. Their work was inspired by several researchers who were previously working on behavior- based navigation approaches such as the use of reactive behaviors or motor schema , the subsumption architecture , a distributed architecture for mobile navigation (DAMN)  and the coordination behavior technique used in their work inspired by Seraji et al. . For the evaluation of their proposed scheme, some typical cases were simulated in which a robot is to move from a given current position to a desired goal in various unknown environment. It was successfully tested, in which the robot managed to navigate its way towards the goal while avoiding obstacles.
D. Shi et al.  presents RobotNavigation in Cluttered 3D environments using preference-based fuzzy behaviors. K. Tanaka  describes an introduction to fuzzylogic for practical applications. X. Yang et al.  present a layered goal-oriented planning strategy for mobile robotnavigation. P.G. Zavlangas et al.  present industrial robotnavigation and obstacle avoidance employing fuzzylogic. Also author took the support of Lab VIEW PID Controller Toolkit User Manual , National Instruments Corporation, Austin. P. F. Muir et al.  presents kinematic modeling of wheeled mobile robots. E. L. Hall et al.  describes motion planning using fuzzylogiccontroller in Robotics: A User-Friendly Introduction. Z. L. Cao et al.  presents dynamic omni- directional vision for mobile robots. Z .L. Cao, Y. Y. Huang, and E. L. Hall  presents region filling operations with random obstacle avoidance for mobile robots. S. J. Oh et al.  presents calibration of an omni-directional vision navigation system using an industrial robot. Kazuo Tanaka  presents design of model-based fuzzycontroller using Lyapunov‟s stability approach and its application to trajectory. C. V. Altrock et al.  presents advanced fuzzylogic control technologies in automotive applications. B. M. Bhairat et al.  describes implementation of crisp logic for robot control. B. M. Bhairat et al.  presents mathematical model for trajectory control using fuzzylogic. B. M. Bhairat et al.  presents steering mobile robot using fuzzylogic approach.
Autonomous mobile robots’ navigation has become a very popular and interesting topic of computer science and robotics in the last decade. Many algorithms have been developed for robot motion control in an unknown (indoor/outdoor) and in various environments (static/dynamic). Fuzzylogic control techniques are an important algorithm developed for robotnavigation problems. The aim of this research is to design and develop a fuzzylogiccontroller that enables the mobile robot to navigate to a target in an unknown environment, using WEBOTS commercial mobile robot simulation and MATLAB software. The algorithm is divided into two stages; In the first stage, the mobile robot was made to go to the goal, and in the second stage, obstacle avoidance was realized. Robot position information (x, y, Ø) was used to move the robot to the target and six sensors data were used during the obstacle avoidance phase. The used mobile robot (E_PUCK) is equipped with 12 IR sensors to measure the distance to the obstacles. The fuzzy control system is composed of six inputs grouped in doubles which are left, front and right distance sensors two outputs which are the mobile robot’s left and right wheel speeds. To check the simulation result for proposed methodology, WEBOTS simulator and MATLAB software were used. To modeling the environment in different complexity and design, this simulator was used. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for autonomous mobile robots and the objective of this research has been successfully achieved. This research also indicated that WEBOT and MATLAB are suitable tools that could be used to develop and simulate mobile robotnavigation system.
The research is motivated by the gap between the current available technology and new Application demands. The current available industrial robots used in production and/or manufacturing lack flexibility, adaptability and autonomy, typically performing pre- programmed sequences of operations as pre-decided by the programmer, in highly constrained environments and these systems prove unable to function in new environments or face unexpected situations. Soft computing techniques like fuzzylogic, which use the tolerance for imprecision inherent in most real world systems, to improve performance of a system in an iterative manner have become hugely popular methods for controllerdesign in mobile robotics. These techniques are used for expressing various subjective uncertainties in human-like behavior. The real world problems cannot be defined in crisp (hard) logic, and are characterized by uncertainties, so a fuzzy based modeling scheme was considered appropriate to design the controller of the robot in order to deal with real life data.
pushes system state variables towards the sliding line. A chattering measure is introduced. The integral of the sliding measure, and performance indicators, including the rise time, error integral and steady state error, are used to define a fitness function in a step reference scenario. The method is tested on the model of a 2-DoF DD (Direct Drive) SCARA type robot, via simulations. The GA-tuned SMC, however, is obtained for a fixed reference signal and fixed payload. Different references and payload values may lead to chattering effects and performance degradation. The second SMC parameter tuning method proposed in the thesis employs a fuzzylogic system to enlarge the operation range of the controller. The chattering measure and the sliding variable are used as the inputs of this system. The fuzzylogic system tunes the controller output smoothing mechanism on-line, which opposes the off-line GA technique. Again, simulations carried out with the Direct-Drive robot model are employed to test the control and the tuning method. The variable sliding control gain and the introduction of a “Smoothing Function” tuned by a GA and a FuzzyLogic System are novel contributions.
One after another the BLDC motor’s electronic commutator energizes the stator coils and generates a rotating electric field that ‘drags’ the rotor around with it. N “electrical rotations” equates to one mechanical rotation, where N is the number of magnet pairs. To indicate the relative position of stator and rotor to the controller three phase effect sensors are embedded in the stator of the three phase brushless DC motor. The hall sensor helps brushless DC motor to energize the winding in a correct sequence and at the correct time.Constructionally the all sensor is mounted on the non driving side of the system [Figure.2].
The concept of FuzzyLogic was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership. This approach to set theory was not applied to control systems until the 70's due to insufficient small-computer capability prior to that time. Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement .
There are numerous advantages to using fuzzy; however, the main one that stands out is the user-friendly logic. The concept that fuzzylogic was built on was to mimic the human thinking process, which is why fuzzylogic transforms numbers into language and the rule base is configured by sentences instead of mathematical equations. Thus, making it not only much more convenient for the users but also faster to program. Another major advantage is that unlike other controllers, fuzzylogic has the ability to work with imprecise mathematical models or ones that are approximated and still provide an efficient output. Fuzzylogic is consistent and robust even during frequently fluctuating applications such as the weather condition with solar panels. Finally, fuzzylogic is capable of working in parallel with other control techniques such as PID or state feedback .
This paper deals with H∞ Filter (HF)-Fuzzylogic based mobile robot localization and mapping as an approach to prevent the Finite Escape Time (FET) problem in HF. The FET problem has been limiting the HF capabilities in estimation for decades and has been one of the important aspects to be considered to ensure HF performs well during mobile robot observations. The proposed technique focusses on the HF innovation stage by including very few FuzzyLogic rules, and fuzzy sets. The design is generally divided into two stages; firstly, the analysis of HF innovation characteristics and then the implementation of FuzzyLogic technique into the system. The analysis also presents the preliminary study on different membership functions to discover the best possible technique to combine with HF based mobile robot localization. The simulation results proved that FuzzyLogic can be used to avoid the FET from occurred while at the same time improving the estimation of both mobile robot and landmarks.
The EKF algorithm will basically prevent the estimated position from drifting. In order to see the effectiveness of the EKF, a simulation made to show how the mobile robot move if it depends only to odometer. Meanwhile another simulation was made with two kinds of motion models, which is the actual and the estimated model. The actual motion model is the motion reference which is represents the real movement of the mobile robot in a noisy environment, where the estimated motion model is the noise-filtered pose of the mobile robot motion. Finally, the mobile robot is programmed to move in a random path in effort to always move close to the actual path.
In this experiment 8 rules are created based on Sugeno approach as shown in Table 1. It creates a simple algorithm, due to only low- cost robot with simple processor and sensor. In this experiment to process Fuzzy-ACO algorithm, 8 bits AT-Mega 16 microcontroller only has 16 Kbytes of RAM and 512 of ROM is utilized (See Figure. 2). For finding the target photodiode sensor is placed at the bottom of the robot. The robot will follow the black dot from photo diode because it reflection of the food of ants. If the food is found, then the Explorer will send the signals via the X-Bee to the Follower. The Fuzzy-ACO algorithm divided into two processes for all environmental situations. First, the fuzzylogic algorithm is activated for avoiding the obstacle and second, the ACO algorithm in the explorer robot active for finding the target and to determine the optimal path. The Fuzzy- ACO algorithm in the followerrobot only active, when the communication signal from the X-Bee is high or “1”, it‟s mean the target have found.
The most difficulties in designing line followerrobot are to design the line followerrobot that can navigate effectively . The navigation of line followerrobot usually are effected by the physicals kinematics constraints which are motor and sensor response, position and the turning radius of the robot. In recent years, the designers have faced problems to design a line followerrobot that can navigate perfectly. In order to improve the navigation reliability of the differential drive for line followerrobot, line sensor configuration is implemented . For electronic components, the robot use VEXTA 15W DC Motor with motor driver card. As mention in this paper, the motor driver card is use because of the ability to control the basic movement, speed feedback and speed control. For master controller, PIC 16F877A has been use due to the reasonable price and easy to obtain. For the sensing method, the line followerrobot use ultra-bright LED combined with a light dependent resistor due to the low cost. In order to overcome different light conditions, a new breed tune-able sensor method is used. The controlling strategy that used in this paper is two controllers which are master controller and slave controller. Therefore, the computational burden of the master controller will be reduced. According to this paper, the suitable sensor array design is to use single line sensor array because is use less sensor and still can navigate effectively.
The software that is installed and run in the robot is collectively known as the robot con- troller. This is made up of various subsystems including communications, motor control, command interpretation, internal interrupt handling and processing. The motor controller used in this experiment is a Proportional, Integral and Differential (PID) controller that uses the independent configuration, also see Chapter 3. The purpose of a PID controller is to reduce the error between the measured variable (MV) of the process and the setpoint (SP) or target of the process to zero. So if the required speed of a robot has been set then the PID controller will act so that the measured speed of the robot equals the required speed. Three terms make up the PID controller. The first term is the Proportional term which is the error between the measured variable and the setpoint multiplied by the Pro- portional parameter (P). The second term is the Integral term which is the sum of errors multiplied by the Integral parameter (I). The third term is the Differential term which is the change in error between this measurement and the previous measurement multiplied by the Differential parameter (D). By altering the values of P, I and D the characteristics of the PID controller are altered. Having investigated the motors and shown that there is no significant difference between them, then it is considered valid to use the same PID controller on both motors. The purpose of these experiments was to determine the best set of P,I and D parameters, out of those tested, to use in the PID motor controller. However it is not intended to find the optimal set of parameters of the PID controller.