The proposed method, in contrast to the existing ones, is of more flex- ible control operations, which can be used in multi-person-operation tele- manipulation tasks. This is implemented by two separate gesture processing channels for steering, and velocity changing with the latter including gear shifting and accelerator/break pressing. The training data set for gear shift- ing gestures can be readily obtained by sampling humangestures and thus an adaptive Cerebellar Model Articulation Controller (CMAC) network is applied in this work for gear shifting gesture classification . However, differently, it is difficult to obtain training data for the steering and veloc- ity control simultaneously, because such type of training data can only be obtained by simultaneously sampling the mobile robot’s moving direction and velocity and their corresponding gestures from the human demonstra- tor. Note that a fuzzy inference system can take the advantage of human driving experiences in the from of fuzzy rules to solve such problem [22, 23]. Thus, the major contribution of this work is a gesture-based control system which integrates a CMAC and a fuzzycontroller (Sections 2.2, 2.3 and 2.4) using humangestures that are similar to practical driving gestures to control a mobilerobot in an efficient and user-friendly way.
The choice of skid-steer configuration other than typical mobility configurations for a mobilerobot platform represented a challenging part of research element to this work. The control problem for a skid-steering mobilerobot is quite challenging mainly because of two facts . Firstly, a skid-steering system is an under-actuated system and secondly, its mathematical model is uncertain. Any approach to controlling a dynamic system needs to use some knowledge, or model, of the system to be controlled. In the case of a robot, this system consists of the robot itself and the environment in which it operates. However, while a model of the robot on its own can normally be obtained, the situation is different when a robot is embedded in the real world and in an unstructured environment. These environments are characterized by the ubiquitous presence of uncertainty or even worse, the nature of the involved phenomena which is not able to be precisely modeled or quantified.
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 mobilerobot enable it to avoid obstacles in a cluttered environment that includes the other mobile robots. A simple adaptivefuzzylogic-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 mobilerobot 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 mobilerobot 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
Robot is a word from the Czech word robota, which means ‘slave laborer’. Czech playwright Karel Capek (1890-1938) made the first use of the word ‘robot’ as a perfect, tireless worker with arms and legs. Referring to Robot Institute of America (RIA) a robot is "A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks". According to Webster a robot is "An automatic device that performs functions normally ascribed to humans or a machine in the form of a human."
There are two types of trajectory tracking controller which it can be used; reactive controller and predictive controller. Reactive controller is used with feedback control system that it generates mobilerobot control signal by depending on the error signal (the difference between desired and actual signal). This means that the correction is done after the error happened, so it is impractical for using with high speed trajectory tracking in mobilerobot application. While predictive controllers, it generates future information. The predicted state depends on the knowledge of the whole trajectory, and the model of mobilerobot takes an advantage of actions that happened before the errors occur. This takes an ability for predictive trajectory tracking controller to be suitable for high speed trajectory tracking mobile in robot applications such as real time applications .
Many efforts are made to replace the conventional power system stabilizers with FuzzyLogic Based, Flexible AC Transmission Systems (FACTS) based and Artificial Neural Network based stabilizers. This is because of the fact that the above new approaches are superior to algorithmic methods and are adaptive in nature. They also have fast response with reduced transients and can also adapt themselves to the non-linearity in the system. The drawback of the fuzzy based and adaptivefuzzy based stabilizers is in the formation of rule base. This rule base may vary from system to system. The limitations of ANN based stabilizers are in its learning ability and suitable learning algorithms are required and this may even system dependent. Many simulated studies are made in this direction and good results are obtained  to .
Soft computing techniques have been shown to be e®ective in dealing with complex and nonlinear behavior of structural control systems. Fuzzylogic controllers can closely imitate human reasoning and control procedures enabling the use of previous experience and experimental results in designing simple and model-free control systems. The main problem relies on the optimization of the fuzzy parameters, in particular the de¯nition of a truthful inference system. In this regard, neuro-adaptive learning techniques such as ANFIS constitute simple methodologies to optimize fuzzy systems. This allows learning information about a dataset in order to compute the membership function parameters that best allow the associated fuzzy inference system to track a given input/output data. ANFIS is a hybrid learning algorithm that combines the backpropagation gradient descent and least squares methods to create a fuzzy inference system whose membership functions are iteratively adjusted according to a given set of input and output data. The reasoning scheme of ANFIS architecture and its inherent variables are shown in Fig. 1, see Refs. 1 and 2. The inherent advantages of these neuro-fuzzy systems make them particularly suitable to develop control systems for structural engineering problems, which typically have uncertain parameters and nonlinear behavior. Besides, fuzzy based controllers allow a model-free estimation of the system and the fuzzycontroller can be developed by encoding the knowledge of the system without the need to state how the outputs depend mathematically upon the inputs.
2.9. Recent Work on Type-2 Fuzzy Sets and Systems 75 The first application of a type-2 fuzzylogiccontroller to an autonomous mobilerobot was implemented by Hagras , who demonstrated that it outperformed a type-1 FLC. The architecture of the controller was based upon interval type-2 fuzzylogic controllers which were used to implement the basic navigation behaviours, and also the coordination of them to produce a type-2 hierarchical FLC. Experiments were carried out in a labora- tory environment and also outdoors. The environments were challenging, dynamic and unstructured in nature. Numerous experiments were carried out including night time op- eration. It was shown that the type-2 controllers dealt in real-time with the uncertainties of the environments. The results obtained showed very good real-time control responses, which had outperformed the equivalent type-1 FLCs and HFLCs. There was about a 64% reduction in the number of rules for the type-2 FLCs and HFLCs to those used in the equivalent type-1 configuration system. The first instance of an industrial DSP embed- ded platform, with a real time type-2 FLC, used to control a marine diesel engine was by Lynch et al , . They found that the type-2 FLCs dealt with the uncertainties in real-time and produced a robust control response. This demonstrated that the embedded type-2 FLCs outperformed the PID and type-1 FLCs previously used to control the ma- rine engine whilst using smaller rule bases. Coupland has shown that the use of geometric methods can resolve the computational overhead required in general type-2 fuzzylogic, and so allow it to be applied to time critical control problems . This was demonstrated in , where a general type-2 FLC outperformed both an interval type-2 and a type-1 FLC, all executing the same tasks. Studies comparing type-2 and type-1 FLC perfor- mance have shown that the best results are given by the type-2 controllers , . In the realm of robot soccer games Figueroa et al.  explored how the type-2 fuzzylogiccontroller overcome the uncertainty in the control loop without increasing the com- putational cost of the application. Hagras recently described a method to develop a type-2 FLC through embedded type-1 FLCs demonstrating that the type-2 FLC outperforms the type-1 FLCs that it was based on .
There are two types of logic: Boolean Logic and FuzzyLogic. Boolean logic deals only with two things – true and false, 1 and 0. The Fuzzylogic allows the true, false and partial true. It functions as per the functioning of the brain providing various results.In simple terms it can be explained that if the food is tasty or not, in terms of Boolean logic it is either tasty or not tasty but in terms of FuzzyLogic there are various options – very much tasty, tasty, less tasty and no tasty. Figure 2 shows the general architecture of the Fuzzylogiccontroller system. A Fuzzylogiccontroller comprises of four parts: Fuzzifier, Rules sets, Interface engines and Defuzzifier.
Dalam masa beberapa tahun kebelakangan ini pengawalan logik fuzzy telah berkembang sebagai salah satu bidang paling aktif dan sangat berkesan dalam pengunaan rekabentuk sistem kecerdasan. Pada masa sekarang, fuzzy telah membolehkan pelbagai kegunaan dalam banyak bidang, iaitu bermula dari pengawalan pemprosesan industri ke pemeriksaan perubatan dan perdagangan keselamatan. Yang paling ditumpukan, satu sistem logik fuzzy telah digunakan untuk pengawalan tidak linear, pengubahan masa, sistem bermasalah, untuk mengawal sistem-sistem dinamik yang tidak begitu dikenali sebagai pengawalan kedudukan servomotor, dan pengawalan tangan robot, dan untuk menangani pembuat keputusan yang kompleks atau sistem-sistem pemeriksaan.
The rest of the thesis is organized as follows. Chapter 2 presents a general literal review on what gestures are and how they are defined in literature, as well as present gesture taxonomies in gesture studies. Furthermore, the concept of a gesture space, in which the gesturing occurs, is explained, showing that gestures are not limited to a particular location in gesture space across different persons. Chapter 3 introduces the notion of attentional models and presents an implementation of an attentional model for a humanoid robot, as a prerequisite for intuitive human-robot interaction. The effect of different behaviors on the way others perceive the robot was tested to identify the influence of particular characteristics of robot’s behaviors. Chapter 4 introduces the notion of gesture vocabularies and how they can be developed for both humans and robots. The procedure is applied on development of a human and a robot gesture vocabulary for a robot-waiter interaction scenario. Chapter 5 presents the gesture recognition and disambiguation framework. Firstly, development of a robust gesture recognition algorithm is presented, focusing on its use in real world scenarios. Most importantly, the algorithm applies one-shot learning, which means that the training is performed using low number of training samples. This is followed by presentation of a theoretical concept for gesture disambiguation. The second part of the chapter introduces an approach for learning the relation between visual sensory input and motor commands, using the example of pointing gestures. Finally, Chapter 6 discusses the obtained results, highlights main accomplishments, what are the important outcomes and what could be possible future research directions based on the presented results.
Now a day’s fuzzylogic is the one of the most successful technology for developing the advanced control systems. Because fuzzylogic is simple to understand and simple to develop. Fuzzylogic control system provides an excellent platform in which human perception based actions can be easily performed. By using a fuzzylogic control system the way human being thinks and makes decisions can be developed and enforced in robotics by simple IF-THEN or IF- ELSE rules and can be mix with easily understandable and natural linguistic illustrations. The present all design analyses are purely mathematical or purely logical based. For these design analyses, we required accurate equations and the output of the solution may or may not give the accurate result. But by using fuzzylogic technique, we can get approximate result of the final solution. The input requires for fuzzylogic system are only some linguistic terms and need not use complex equations.
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the traditional method has some errors in practical applications, and the character of real-time is not obvious. For these reasons, fuzzylogic has attracted much attention because it does not need to establish accurate mathematical model, deal with uncertain information easily and can transform expert knowledge into input directly and so on. As the result of the sensor information obtained by the autonomous mobilerobot is dynamic and uncertain in the process of moving , this paper uses the method based on fuzzylogic to track the path, and it can deal with the uncertain situation well. What’s more, it realizes the autonomous navigation of the mobilerobot.
Abstract - A reactive fuzzylogic based control strategy was developed for mobilerobot navigation. To decrease the number of fuzzy rules and related processing, a RAM-based neural network was combined with the fuzzylogic strategy. The fuzzy rules are used to interpret sensor information. The neural network uses results from the fuzzylogic as well as environmental information to make navigation decisions. The feasibility of this neuro-fuzzy approach was demonstrated on a mobilerobot using a simple, 8-bit microcontroller. Experiments show the approach works well, as the robot was able to successfully avoid objects while seeking a goal in real-time. The neuro-fuzzy approach is code-efficient, fast, and easy to relate to the physical world.
I declare that this report entitled “The Integration of FuzzyLogic System for Obstacle Avoidance Behaviour of MobileRobot” is the result of my own research except as cited in the references. The report has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.
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 robot navigation problems. The aim of this research is to design and develop a fuzzylogiccontroller that enables the mobilerobot to navigate to a target in an unknown environment, using WEBOTS commercial mobilerobot simulation and MATLAB software. The algorithm is divided into two stages; In the first stage, the mobilerobot 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 mobilerobot (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 mobilerobot navigation system.
As an automatic machine, a mobilerobot is able to understand the sensed information to receive the knowledge of its location. It is also able to plan a real- time path from a starting position to goal position with obstacle avoidance capability, as well as to control the robot steering angle and its speed to reach the target. Mobile robots could be utilized in different applications such as monitoring, transportation, and many other potential applications. The ability of a mobilerobot to navigate autonomously has improved tremendously due to the improvement of various path planning and obstacle avoidance algorithms developed by recent researchers. A mobilerobot needs a robust controller to adapt the fast integration between the input and output due to the navigation in an uncertain environment. Due to nonlinearity property of mobilerobot, it is difficult to obtain an absolute mathematical model of a system for designing its controller . Many mobile robots use a drive mechanism known as the differential drive where each wheel is independently driven by an actuator. Thus, the direction of a mobilerobot can be controlled by
To demonstrate the results, three different cases of different fuzzy sets effects are observed to identify the FuzzyLogicController influence in controlling the gripper. The analysis through MATLAB Simulink and SimMechanics Toolboxes virtually identifies the best selections of fuzzy sets design to be incorporated into the grasping system when a cube shape and rectangle shape objects are lifted. The selections are based on the time consumed during operation. Analysis through SimMechanics is still unavailable which considerably makes this investigation become interesting as designer could design the system with exact parameters before realizing the design. Hence, the design will improve the performance with less expenditure.
The aim of this study is to develop a novel fuzzy-adaptive control law in order to increase tracking performance of the robot manipulators. For this purpose, a fuzzylogic control rule is designed for control parameter L. In the previous study , the control gain is constant. The novelty of this project is that the control gain is defined by fuzzylogic controllers. As shown from Fig. 4-11, tracking performance of the proposed fuzzy-adaptive control law is better than the developed adaptive control law (9). Tracking performance of the controller changes depending on the values of the control gain L, and tracking performance can be improved by using fuzzylogiccontroller for the control gain to appropriate values. As seen from Fig. 4-11, the proposed fuzzy-adaptive control law increases tracking performance of the system.