I certify that a Thesis Examination Committee has met on 12 April 2013 to conduct the final examination of Mohsen Shayestegan on his thesis entitled “FuzzyLogicController for RobotNavigation in an Environment with Obstacles and DeadEndTraps” in accordance the Universities and University colleges Act 1971 and the Constitution of the Universiti Putra Malaysia [P.U. (A) 106] 15 March 1998. The Committee recommends that the student be awarded the Master of Science.
When the mobile robot is traveling toward its final target in unknown environment , it faces different shapes of obstacles in different location . Obstacles are detected by nine IR sensors which send information of distance between obstacle and mobile robot to a fuzzylogiccontroller. Fuzzylogic control (FLC) is adopted to control the movements of the right and left wheels. The two outputs are the motor commands to both the left an right motors.In this way, the mobile robot can avoid obstacles autonomously.
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.
mobile robots by using infrared sensors is designed in . Intelligent controllers for target tracking, wall following are all based on the concept of fuzzy sliding mode control (FSMC). The fuzzy target tracking control unit (FTTCU) consisted by the behavior network and the gate network has been applied to the target tracking control with obstacle avoidance. A fuzzy behavior-based architecture is designed in  for the control of mobile robots in a multi-agent environment. It decomposes the complex multi-robotic system into smaller modules of roles, behaviors and actions. Fuzzylogic is applied to implement individual’s behaviors to coordinate the various behaviors, to select roles for each roles, for robot perception, decision making and speed control. Navigation of mobile robots using fuzzylogiccontroller is presented in . Fuzzy rules embedded in the controller of a mobile robot enable it to avoid obstacles in a cluttered environment that includes the other mobile robots. A simple adaptive fuzzylogic-based controller is designed in  which utilizes a fuzzy interference system (FIS) for estimating the non-linear robot functions involving unknown robot parameters for tracking control of wheeled mobile robots. A multi-agent architecture with cooperative fuzzy control for a mobile robot is developed in  in order to centralize the coordination of the behaviors in a single module or agent which represents a problem when there is more than one behavior competing for the available resources. A path following approaches for mobile robot based on fuzzylogic set of rules is developed in  to emulate the human driving behavior. The input to the develop fuzzy system is the approximate information concerning the next bend ahead the robot and the corresponding output is the cruise velocity
Generally, all previous works are based on one of the following approaches: the first approach is based on computational geometry and the second one draws upon heuristics method. The former approach has a richer background. The second approach contains soft computing method (Fuzzylogic, Genetic algorithm and neural network). In the middle of 90s, a new approach was introduced which combined computational geometry and probability. The purpose of proposing this approach was to decrease the complexities in configuration space and search in the methods of the first approach . Many methods are presented based on these approaches. However, they can’t solve the problem in general. For example, some methods limit the work space of robot to two dimension and the obstacles to polygons. Although there are differences among these methods, they share three bases; road map, cell decomposition, and potential field approaches. The exact cell decomposition and road map can’t solve the problem in complex environment due to many of computations. Potential field method is frequently used for local navigations because of its simplicity. But it has stopping problem in local minimum. In the last decades using soft computing methods becomes noticeable. These methods, that contain fuzzylogic, neural networks and genetic algorithm, try to decrease the complexity of path computation, modeling the obstacles and learning. So, faced with dynamic environments in which the obstacles move and the robot must find the alternative paths in a short time without collision with the obstacles, soft computing must be used . For example , which is one of the first methods based on soft computing, used a combined method of genetic algorithm and fuzzylogic. In this method, it is supposed that the target is stable but obstacles are dynamic and movable. This method uses genetic algorithm for calibrating state space variables factors, and uses fuzzylogiccontroller rules set for navigating the robot between moving obstacles.
The neuro-fuzzynavigation system is shown in Fig. 1. The robot detects its environment using two groups of sensors. Four IR range sensors detect obstacles nearby and four photoelectric detectors find the orientation of the light source placed on the goal. Navigation is performed using two fuzzylogic controllers, one to process information from the left side of the robot and another to deal with the right. The fuzzylogic controllers consist of a rule base that fuzzifies range values and goal direction and produces defuzzified control actions. In addition, the controllers implement a reactive control strategy. For example, if the robot finds an obstacle in front of it and in the path to the goal, both fuzzylogic controllers will determine the best steering direction and speed to avoid collision while heading to the goal.
In applications of fuzzylogic to 2D robot motion planning, work in this area is focused on short range reactive control. That is, robots were navigated by simply reacting to near obstacles upon detection while taking into account a global goal direction. While such algorithms have frequently proven effective, they often encounter situations in which the goal configuration becomes unreachable by the robot despite the availability of a traversable path. More commonly, reactive fuzzynavigation may suffer from “shortsighted” behavior wherein the angle to the final goal influences all steering decisions in unison with local sensor data. Situations then arise in which short range sensors may not detect obstacles between the current configuration of the robot and the goal. In these cases, a path may be selected that is less desirable than others available. Figure 2 compares the results of shortsighted behavior to an end-to-end path plan. Through purely reactive short-range fuzzy control, the robot attempts to move in the direction of the goal at point B when obstacle 2 is still out of its perceptive range. The undesirable path ABCDEG is the result. Clearly, with the benefit of long-range planning, path AJKG would be seen as more desirable.
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-follower robot. This FLC design is the improvement of FLC application in a single two differential-driven mobile robot. The relation between leader and follower robot 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.
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.
NIT Rourkela Page 44 In this project, we presented the navigation of four-wheel mobile robot by using sensor based fuzzy PWM controller in an unknown environment. We have studied the structure of the kinematic model of four-wheel mobile robot. By writing the code in the MATLAB, the simulation work done with avoiding the obstacle in the path. In this, we are using fuzzylogic technique for the navigation autonomous four-wheel mobile robot in unknown environment. The simulation results showed that the mobile robot reach the destination without collision with the obstacle and experiments are carried out in the lab. The comparison between experimental and simulation results shown. The error obtained between simulation and experimental is about 8% approximately. The goal of this mobile robot is to reach the destination with avoiding the obstacles in the path.
Two input variables whose values are defined represent the fuzzy sets. These variables have range definitions. The output variables is also defined by a fuzzy set. Four membership functions and truth values were defined over these ranges. The operational rules were applied to generate a result for each rule before a combined operational rules were applied which then combines the results of the rules [7,8,9] . The inputs variables were loads and temperature derivable from sensors. The output of the controller is the quantity that controls the speed of the fan. The load quantity for the computer ranges from 0% to 66.7%. The temperature quantity ranges from 39 o C to 56 o C. The output quantity which is the speed ranges from 644 revolutions per minute(r.p.m) to 745 revolutions per minute(rpm).
Each one of these characteristics uses the information from sensors and find out its remedies and action. The avoidance of obstacle characteristics uses vary the range of different sensors to calculate the distances to the close one obstacle; the target seeking characteristics uses the digital compass which measures the direction of the target and the overturning avoidance characteristics uses a speedometer which gives the reading to calculate the mobile robot speed. Confined avoidance of obstacle is a primary difficulty in the navigation of mobile robot.  Majority navigation problems of mobile robot done in the surrounding which known to robot and with the help sensors robot find a practicable free path travelling towards the target and avoiding the obstacles. On the other side when mobile robot has to travel in the environment that is totally or to some extent unknown then local navigation methodologies are exceptionally significant for the mobile robot to productively accomplish its targets.
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.
Abstract - The paper proposes the realization of a FuzzyLogic Temperature Controller. In this paper an analysis of FuzzyLogicController is made and a temperature controller using MATLAB is developed. Here we used FuzzyLogic Toolbox which is very useful software for development and testing of FuzzyLogic system. It can be very quickly implemented and its visual impact is very encouraging. In this controller the Rule Base, membership functions and inference engine are developed either using digital systems such as memory and logic circuit or it can be developed using analog CMOS circuits. Analog Fuzzy systems are popular because of their continuous-time-processing and high frequency and low power implementation.
capability to understand the systems behavior. Besides, this control technique is based on qualitative control rules. This kind of approach depends on the basic physical properties of the systems, and it is potentially able to extend control capability even to those operating conditions where linear control techniques fail. As a consequence, the application of nonlinear control laws to face the nonlinear nature of balancing robot is easy since fuzzy control is based on heuristic rules. In fact, the FLC approach is general in the sense that almost the same control rules can be applied to a non-linear balancing robot system. It is possible to give two inputs to the FLC as shown in Figure 3. The proposed defuzzification methods for the FLC are sugeno or mamdani. This is because both of these techniques are commonly used in designing the FLC. In order to implement 6 inputs to the controllers, the FLC were divided into three. As illustrated in Figure 3, the ‘FLC 1’controls the linear position on x-axis, ‘FLC 2’ controls the angular position y-axis and ‘FLC 3’ controls rotational angle on z- axis of the balancing robot. The ‘FLC 1’ received the difference (error signal) between position of cart and set point position, x and the rate at which the error of position changes, Δx as the inputs while the ‘FLC 2’ received the angle error and rate of error of pendulum pole as the inputs while ‘FLC 3’ received the error and rate of error of rotational angle about z-axis. The control variables of all FLCs were summed together before converted into voltage signal. This signal is then supplied to the dc motors on both left and right sides of the balancing robot.
The real swarm robot experiment using TFLS1 and IT2FLS algorithms can be seen in Figure 8. The experiment was done using 3 robots with three ultrasonic sensors, one compass sensor and one X-Bee in each robot. On the actual robot with the shape of circular, it has 15 cm diameter and 17 cm height and it using three wheels. Two wheels behind the robot have a function as a controller, and the third wheel used to make the robot move freely. By connecting the two dc motor with the two driving wheels respectively, the rotation direction of each motor controlled by the direction of a drive current of the dc motor and duty cycle of Pulse Width Modulation (PWM), controls the rotation speed of the driving wheels.
Regarding the membership type used in FuzzyLogic, other choices than the triangular membership are available such as the gaussian membership, trape- zoid membership or sigmoid membership. As explained above, researcher usu- ally applies the triangular membership as it is seems to be easier than other membership. Even if this is the factor, there were also some other ﬁndings that discovered very interesting results as shown by V.O.S Olunloyo et al.. Based on their ﬁndings, the triangular membership can exhibit linguistic error as it may not deﬁnes properly the real system conditions. As a result, the gaussian membership can be the best membership to describe any practical system for most engineering application. Moreover, gaussian membership may surpassed triangular membership function if better tracking performance is being priori- tized.
Abstract- The main objective of path planning is to acquire a collision free path for a mobile robot operating in various environments. Diverse approaches and techniques have been implemented to solve a path planning problem by considering certain factors like obstacle shape, its orientation, type of environment etc. Based on the surrounding environment the robot navigates globally or locally. This paper focuses on the navigation of mobile robot operating in a static environment consisting of elliptical and polygonal obstacles. A mathematical formulation has been developed to obtain these paths and also to find the shortest path among them using Centre of Gravity Approach (CGA) and Coordinate Reference Frame (CRF) technique. The simulation results prove the proposed approach to be effective as the robot navigates to the defined target point without colliding with the obstacles in the environment.
A comparative study is carried out between Bug algorithm and Virtual goal method to avoid obstacle when a mobile robot is seeking a goal. From the above plots and experimental results it can be observed that Virtual goal method is dominant over Bug algorithm. Bug algorithm is just able to avoid obstacles but Virtual goal method is capable of even avoiding local minima meaning Virtual goal method does not let robot stuck around corner like objects. The virtual goal method and control system thus implemented is shown to be globally stable if there are no any obstacles present in the environment, and able to leave all obstacles behind. Therefore, the Virtual goal approach guarantees that the robot will reach any reachable goal.
built with fuzzylogic, and vice-versa. However, in a number of cases, conventional design methods would have been overly complex and, in many cases, might prove simpler, faster and more efficient. The key to successful use of fuzzylogic is clever combination with conventional techniques. Also, a fuzzy system is time-invariant and deterministic. Therefore any verification and stability analysis method can be used with fuzzylogic too.