A comparative study of fuzzy logic controllers for autonomous robots.

This paper presents the results from an experimental comparison of a number of fuzzy logic controllers performing mobile robot navigation. An experiment is described which requires the robot to complete a complex, measurable navigational task. The world's first generalised type-2 fuzzy logic controller is compared to a type-1 and a type-2 interval controller. Visual and statistical analyses show that the generalised type-2 fuzzy controller gives a better performance in consistency and smoothness. The impact of this paper comes primarily from the rigorous use of statistical analysis in mobile robot navigation

Mobile Robot Feature-Based SLAM Behavior Learning, and Navigation in Complex Spaces

Learning mobile robot space and navigation behavior, are essential requirements for improved navigation, in addition to gain much understanding about the navigation maps. This chapter presents mobile robots feature-based SLAM behavior learning, and navigation in complex spaces. Mobile intelligence has been based on blending a number of functionaries related to navigation, including learning SLAM map main features. To achieve this, the mobile system was built on diverse levels of intelligence, this includes principle component analysis (PCA), neuro-fuzzy (NF) learning system as a classifier, and fuzzy rule based decision system (FRD)

Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques

The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques

A new fuzzy set merging technique using inclusion-based fuzzy clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets

Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

An hybridization of global-local methods for autonomous mobile robot navigation in partially-known environments

This paper deals with the navigation problem of an autonomous non-holonomic mobile robot in partially-known environment. In this proposed method, the entire process of navigation is divided into two phases: an off-line phase on which a distance-optimal reference trajectory enables the mobile robot to move from an initial position to a desired target which is planned using the B-spline method and the Dijkstra algorithm. In the online phase of the navigation process, the mobile robot follows the planned trajectory using a sliding mode controller with the ability of avoiding unexpected obstacles by the use of fuzzy logic controller. Also, the fuzzy logic and fuzzy wall-following controllers are used to accomplish the reactive navigation mission (path tracking and obstacle avoidance) for a comparative purpose. Simulation results prove that the proposed path planning method (B-spline) is simple and effective. Also, they attest that the sliding mode controller track more precisely the reference trajectory than the fuzzy logic controller (in terms of time elapsed to reach the target and stability of two wheels velocity) and this last gives best results than the wall-following controller in the avoidance of unexpected obstacles. Thus, the effectiveness of our proposed approach (B-spline method combined with sliding mode and fuzzy logic controllers) is proved compared to other techniques

Implementation of Navigation Target Seeker Mobile Robot Based on Pattern Recognition with Fuzzy Kohonen Network (FKN) Methods

Navigation is a technique for determining the position and direction of travel in the actual environment. This navigation system can be implemented on a mobile robot to accomplish a specific task, in this paper is used in order to navigate the robot can move toward a specific target while avoiding obstacles exist. One of the control techniques used in mobile robot navigation is based on pattern recognition techniques. With a pattern that has been previously implanted in the “brains” of the robot, the mobile robot can take action in accordance with the movement of the pattern. This paper used method of Fuzzy Kohonen Network (FKN) in order to be able to navigate a mobile robot to recognize patterns in the environment. The target used is a specific point designated position using a camera support (GPS Ad-hoc). Based on test results using this method, the obtained results are satisfactory, precisely to the targets and fast search time target

Implementation of Navigation Target Seeker Mobile Robot Based on Pattern Recognition with Fuzzy Kohonen Network (FKN) Methods

Navigation is a technique for determining the position and direction of travel in the actual environment. This navigation system can be implemented on a mobile robot to accomplish a specific task, in this paper is used in order to navigate the robot can move toward a specific target while avoiding obstacles exist. One of the control techniques used in mobile robot navigation is based on pattern recognition techniques. With a pattern that has been previously implanted in the “brains” of the robot, the mobile robot can take action in accordance with the movement of the pattern. This paper used method of Fuzzy Kohonen Network (FKN) in order to be able to navigate a mobile robot to recognize patterns in the environment. The target used is a specific point designated position using a camera support (GPS Ad-hoc). Based on test results using this method, the obtained results are satisfactory, precisely to the targets and fast search time target

An Adaptive Mobile Robot with Gaussian type on Fuzzy Logic Type 2

This is an adaptive Mobile Robot Navigation project based on Fuzzy Logic Type 2. The goal of this study is to investigate the performance of a mobile robot in an environment (navigation). As a result, this project will be emphasized on the outcomes of simulation for the mobile robot in navigation. The background for the simulation will be based on the obstacle avoidance. In brief, when the fuzzy controller detects any potential obstacle nearer or on the way for the robot going to the goal point, the robot will be able to avoid it. In navigation, the surrounding environment for the robot and the position of the obstacle should be understood ahead of time. The robot is required to navigate to its destination by avoiding the obstacle. In such, the robot navigation in simulations can be estimated using prior information of the coordinates from the beginning point, the goal point, and the obstacle position. Thus, in this research, the cost function method was implemented to evaluate and estimate the robot's surroundings in a simulated environment. Consequently, the objective of the project is to design a mobile robot in navigation using the fuzzy logic system and to develop the lower state estimation error for both estimated and measured simulation value. By using the cost function and fuzzy logic, the mobile robot navigation was proved as the result shows that the robot was able to avoid the obstacle on its way toward the goal point. Furthermore, the graph shows only a slight difference occurred between the measured and estimated values, indicating that the project was implemented as required