Fuzzy Behavior-based Control Research Articles

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Aiming at the strong dependence on environmental information in traditional algorithms, the path planning of basketball robots in an unknown environment, and improving the safety of autonomous navigation, this article proposes a path planning algorithm based on behavior-based module control. In this article, fuzzy control theory is applied to the behavior control structure, and these two path planning algorithms are combined to solve the path planning problem of basketball robots in an unknown environment. First, the data of each sensor of the basketball robot configuration are simply fused. Then, the obstacle distance parameters in the three directions of front, left, and right are simplified and fuzzified. Then combined with the target direction parameters, the speed, and steering of the basketball robot are controlled by fuzzy rule reasoning to realize path planning. The simulation results show that the basketball robot can overcome the uncertainty in the environment, effectively achieve good path planning, verify the feasibility of the fuzzy control algorithm, and demonstrate the validity and correctness of the path planning strategy.

Fuzzy Behavior-based Control of Three Wheeled Omnidirectional Mobile Robot

In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot (TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static or dynamic obstacles along its path. A simple controller design is adopted, and to do so, two fuzzy behaviors “Track the Target” and “Avoid Obstacles and Wall Following” are considered based on reduced rule bases (six and five rules respectively). This strategy employs a system of five ultrasonic sensors which provide the necessary information about obstacles in the environment. Simulation platform was designed to demonstrate the effectiveness of the proposed approach.

A New Approach of Fusion Behavior-Based Fuzzy Control for Mobile Robot Navigation

A new fusion approach for a finite number of behaviors for mobile robot is presented. The proposed method can enable multiple behaviors. The degree of activation (DA) of each behavior is handled intelligently to ensure the autonomous navigation of a mobile robot in a dynamic and uncertain environment. Simulation results clearly show the capacity of the new algorithm to manage and merge more than two behaviors at the same time. The general algorithm is based on five fuzzy controllers that will lead the robot to reach a destination from a starting point, while avoiding static and dynamic obstacles, as well as the holes avoidance during its navigation.

WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER

Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

Goal-seeking Behavior-based Mobile Robot Using Particle Swarm Fuzzy Controller

Behavior-based control architecture has successfully demonstrated their competence in mobile robot development. Fuzzy logic system characteristics are suitable to address the behavior design problems. However, there are difficulties encountered when setting fuzzy parameters manually. Therefore, most of the works in the field generate certain interest for the study of fuzzy systems with added learning capabilities. This paper presents the development of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A goal-seeking behaviors based on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations and experiments with MagellanPro mobile robot have been performed to analyze the performance of the algorithm. The promising results have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

Behavior-Based Fuzzy Control for Mobile Robot Navigation

A new behavior-based fuzzy control method for mobile robot navigation is presented. It is based on behavioral architecture which can deal with uncertainties in unknown environments and has the ability to accommodate different behaviors. Basic behaviors are controlled by specific fuzzy logic controllers, respectively. The proposed approach qualifies for driving a robot to reach a target while avoiding obstacles in the environment. Simulation and experiments are performed to verify the correctness and feasibility of the proposed method.

Behavior-Based Fuzzy Control of Obstacle Avoidance for Indoor Mobile Robot

This paper describes indoor mobile robot covering path to avoid obstacle based on behavior fuzzy controller. The robot measures the distance to obstacle with ultrasonic sensors and infrared range sensors, and the distance is the input parameter of the behavior-based fuzzy controller. The behavior architecture has three levels behaviors: emergency behavior, obstacle avoidance behavior, and task oriented behavior. The task oriented behavior is the highest level behavior, and has two subtasks: wall following and path covering. The middle level behavior is obstacle avoidance. The lowest level is an emergency behavior, which is the highest priority behavior. The simulation result demonstrates that each behavior works correctly.

A Fuzzy Behavior-Based Control for Mobile Robots Using Adaptive Fusion Units

It is known that a behavior-based control approach is effective for acquiring an intelligent control system of robots. However, further improvements are required for making any behavior-based control system robust against changes in the environments. A module learning method has been applied in the framework of fuzzy behavior-based control to have an adaptive behavioral fusion. In this paper, an adaptive fusion strategy is proposed to adaptively select a cooperative fusion unit or competitive fusion unit, depending on the external sensor information. Some simulations are given to illustrate that the present control systems are flexible against the change of environments or untrained environments, compared to those with a conventional priority-based fusion unit.

Fuzzy Behavior-Based Control of Mobile Robots

An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment. The behavior-based architecture decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions. Fuzzy logic is used to implement individual behaviors, to coordinate the various behaviors, to select roles for each robot and, for robot perception, decision-making, and speed control. The architecture is implemented on a team of three soccer robots performing different roles interchangeably. The robot behaviors and roles are designed to be complementary to each other, so that a coherent team of robots exhibiting good collective behavior is obtained.

Fuzzy Behavior-Based Control. 3rd Report. Module Learning and Behavioral Coordination by an Adaptive Fusion Unit with External Sensor Information.

In order to obtain more effective coordinated behaviors against changes in environments or unlearned environments, we here propose a new fusion unit using external sensor information for a fuzzy behavior-based control system trained in the framework of module learning. The present fusion unit falls into a class of adaptive priority-based architecture in which the fusion between behavioral elements is carried out by a cooperation or comnetition strategy, depending on whether an objective point or obstacle can be detected or not. Some experimental results of a miniature mobile robot Khepera show that the proposed metcod is superior to the conventional method that applies a fusion unit based on a fixed priority-based architecture.

Intelligent control of nonholonomic mobile robots with fuzzy perception

This paper is focused on the intelligent control of vehicles with nonholonomic constraints. A new method to compute a fuzzy perception of the environment is presented, dealing with the uncertainties and imprecisions from the sensorial system and taking into account nonholonomic constraints of the vehicle. This fuzzy perception is used, both in the design of each reactive behavior and solving the problem of behavior combination, to implement a fuzzy behavior-based control architecture. Furthermore, teleoperation and planned behaviors, together with their combination with reactive ones, are considered; improving the capabilities of the intelligent control system and its practical application. Experimental results, of an application to control the ROMEO-3R autonomous vehicle, demonstrate the robustness of the proposed method.

Adaptive Behavioral Fusion of Fuzzy Behavior-Based Control for Khepera

The most significant features of a behavior-based control system are the structure, the behavioral fusion or coordination, and the learning algorithm. Especially, the intelligent behavior of a robot is relying on the behavioral fusion. An adaptive behavioral fusion method for the fuzzy behavior-based control is studied in this paper. One of the conventional fusion methods is the priority based fusion. However, there exists limits in such methods in dynamic environments in addition to those of sensor's activity. The present method changes the priority of behavior groups according to the sensor's activity. The efficiency of the proposed method is illustrated with some experimental results of a miniature mobile robot called Khepera.

Fuzzy Behavior-Based Control. 2nd Report, Learning with a Virus-Evolutionary Genetic Algorithm with Species.

In this paper, we describe a learning of fuzzy behavior-based control system by applying a virusevolutionary genetic algorithm with species. We first state the development of a virus-evolutionary genetic algorithm with species(VEGAS), in which one population is divided into some subpopulations(i.e., species)so that a virus infection between species can be introduced as well as a virus infection within a species. We then apply the VEGAS to the learning of a fuzzy behavior-based control system for a mobile robot with two independent driving wheels, where a control problem of acquiring multi-objective points is considered. The effectiveness of the present learning method is demonstrated by a set of computer simulations, compared to the conventional method with a simple GA.

Fuzzy Behavior-Based Control for a Task of Three-Link Manipulator with Obstacle Avoidance

Our objective is for a 3-link manipulator to reach a target while avoiding obstacles with online information using a fuzzy-behavior-based control approach. Control applied to mobile robots elsewhere is modified to suit to the manipulator. Fuzzy behavior elements are trained using a genetic algorithm. A component apart from the basic concept is introduced to overcome gravitation. Result shows the manipulator reaches the target with an acceptable solution for 3 simulations, so the proposed approach is suitable to multilink manipulator task control.

Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots

Intelligent control techniques for robotic systems have been used with some success in a wide variety of applications. In this paper, we construct a method for the intelligent control system of a robot using the fuzzy behavior-based control, which decomposes the control system into several elemental behaviors, and each one is realized by fuzzy reasoning. In particular, a module learning method is investigated for obtaining each representative group behavior, so that the robot can, consequently, acquire more general knowledge or fuzzy reasoning, than a central learning method. The proposed method is applied for an obstacle-avoidance problem of a mobile robot; the effectiveness of the method is illustrated through some simulations.

Evolutionary Strategy Using Statistical Information and Its Application to Mobile Robot Control

We describe an evolution strategy (ES) using the statistical information of subgroups obtained automatically by a similarity metric of individuals at each generation. Arithmetical crossover is done with an elite individual and a mean individual within each subgroup to produce offspring. Standard deviation calculated within a subgroup is used in mutation. The effectiveness of the proposed ES is first shown with tests of the 5 De Jong functions. The present ES is also applied to the acquisition of control for a terminal control problem in an omnidirectional mobile robot, in which robot control is based on fuzzy behavior-based control that combines subsumption-like architecture and fuzzy reasoning.

Fuzzy Behavior-Based Control. 1st Report. A Proposal of Control System Realization.

Fuzzy Behavior-Based Control. 1st Report. A Proposal of Control System Realization.