Obstacle Avoidance during Robot Navigation in Dynamic Environment using Fuzzy Controller

A Simulink model containing fuzzy logic controller for collision-free robot navigation in a dynamic environment is presented in this paper. Two controllers, pure pursuit and fuzzy logic controller, are considered to handle robot navigation with obstacle avoidance. Ignoring the obstacles, the pure pursuit controller computes the required linear and angular velocities to direct robot from start to goal location. However, if obstacles are present in the navigation path then the robot will get collided with obstacles in the path. As a result, the robot will not reach to the provided goal location. The fuzzy logic controller is used to avoid obstacles in the navigation path. The fuzzy logic controller takes obstacle distance, obstacle angle, target direction and the x coordinate of goal location as inputs. Consequently, the fuzzy logic controller outputs the required change in angular velocity for the robot. This change in angular velocity is applied to the angular velocity provided by the pure pursuit controller. The experimental work is performed using Turtlebot Gazebo simulator. The navigation including environment, obstacles and resultant paths are also manifested.

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Robot navigation with obstacle avoidance in unknown environment

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.14767 ◽

2018 ◽

Vol 7 (4) ◽

pp. 2410 ◽

Cited By ~ 1

Author(s):

Neerendra Kumar ◽

Zoltán Vámossy

Keyword(s):

Fuzzy Logic ◽

Obstacle Avoidance ◽

Fuzzy Inference System ◽

Fuzzy Logic Controller ◽

Fuzzy Inference ◽

Fuzzy Controller ◽

Robot Navigation ◽

Training Data ◽

Inference System ◽

Pure Pursuit

In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

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Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2018-0038 ◽

2019 ◽

Vol 20 (1) ◽

pp. 51-62

Author(s):

Rajmeet Singh ◽

Tarun Kumar Bera

Keyword(s):

Fuzzy Logic ◽

Mobile Robot ◽

Dynamic Model ◽

Obstacle Avoidance ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Bond Graph ◽

Membership Functions ◽

Current Position ◽

Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

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Hybrid Neural-Fuzzy Controller for Motorized Robot Arm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.5068 ◽

2011 ◽

Vol 403-408 ◽

pp. 5068-5075

Author(s):

Fatma Zada ◽

Shawket K. Guirguis ◽

Walied M. Sead

Keyword(s):

Fuzzy Logic ◽

Design Methodology ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

System Response ◽

Robot Arm ◽

Neural Controller ◽

Hybrid Controller ◽

Neural Fuzzy ◽

Transient And Steady State

In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

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Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

Mathematical Problems in Engineering ◽

10.1155/2010/578406 ◽

2010 ◽

Vol 2010 ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Yi Fu ◽

Howard Li ◽

Mary Kaye

Keyword(s):

Fuzzy Logic ◽

Stability Analysis ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Intelligent Vehicles ◽

Intelligent Vehicle ◽

Rule Base ◽

Control Laws ◽

Robotic Vehicle ◽

Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

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DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽

10.31000/jt.v9i2.3676 ◽

2020 ◽

Vol 9 (2) ◽

Author(s):

Sumardi Sadi

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Fuzzy Logic Control ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Dc Motor ◽

Motor Speed ◽

Logic Control ◽

Control Command ◽

Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

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An intelligent lead-acid battery closed-loop charger using a combined fuzzy controller for PV applications

E3S Web of Conferences ◽

10.1051/e3sconf/202129701033 ◽

2021 ◽

Vol 297 ◽

pp. 01033

Author(s):

Iliass Rkik ◽

Mohamed El khayat ◽

Hafsa Hamidane ◽

Abdelali Ed-Dahhak ◽

Mohammed Guerbaoui ◽

...

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Control Unit ◽

Buck Converter ◽

Lead Acid Battery ◽

Charging System ◽

Pv Array ◽

Full Power ◽

Lead Acid

This paper presents the modeling of an intelligent combined MPPT and Lead-Acid battery charger controller for standalone solar photovoltaic systems. It involves the control of a DC/DC buck converter through a control unit, which contains two cascaded fuzzy logic controllers (FLC), that adjusts the required duty cycle of the converter according to the state of charge and the three stage lead acid battery charging system. The first fuzzy logic controller (FLC1) consists of an MPPT controller to extract the maximum power produced by the PV array, while the second fuzzy controller (FLC2) is aimed to control the voltage across the battery to ensure the three stage charging approach. This solution of employing two distinct cascaded fuzzy controllers surmounts the drawbacks of the classical chargers in which the voltage provided to the lead acid battery is not constant owing to the effects of the MPPT control which can automatically damage the battery. Thus, the suggested control strategy has the benefit of extracting the full power against the PV array, avoiding battery damage incurred by variable MPPT voltage and increasing the battery’s lifespan.

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Control of Rotary Cranes Using Fuzzy Logic

Volume 6C: 18th Biennial Conference on Mechanical Vibration and Noise ◽

10.1115/detc2001/vib-21598 ◽

2001 ◽

Author(s):

Amjed A. Al-mousa ◽

Ali H. Nayfeh ◽

Pushkin Kachroo

Keyword(s):

Fuzzy Logic ◽

Closed Loop ◽

Fuzzy Logic Controller ◽

Fuzzy Inference ◽

Fuzzy Controller ◽

Operating Conditions ◽

Building Construction ◽

Loop Control ◽

Industrial Structures ◽

Inference Engines

Abstract Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them became difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control preferable. In this work a fuzzy logic controller is introduced with the idea of split-horizon; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FTEs). Computer simulations are used to verify the performance of the controller. Three simulation cases are introduced: radial, compound, and damping. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing them in a relatively reasonable time.

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Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-05-2021-0091 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Abhishek Kumar Kashyap ◽

Dayal R. Parhi

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Search Algorithm ◽

Optimal Path ◽

Humanoid Robots ◽

Steering Angle ◽

Content Type ◽

Hybrid Controller ◽

Humanoid Nao

Purpose This paper aims to outline and implement a novel hybrid controller in humanoid robots to map an optimal path. The hybrid controller is designed using the Owl search algorithm (OSA) and Fuzzy logic. Design/methodology/approach The optimum steering angle (OS) is used to deal with the obstacle located in the workspace, which is the output of the hybrid OSA Fuzzy controller. It is obtained by feeding OSA's output, i.e. intermediate steering angle (IS), in fuzzy logic. It is obtained by supplying the distance of obstacles from all directions and target distance from the robot's present location. Findings The present research is based on the navigation of humanoid NAO in complicated workspaces. Therefore, various simulations are performed in a 3D simulator in different complicated workspaces. The validation of their outcomes is done using the various experiments in similar workspaces using the proposed controller. The comparison between their outcomes demonstrates an acceptable correlation. Ultimately, evaluating the proposed controller with another existing navigation approach indicates a significant improvement in performance. Originality/value A new framework is developed to guide humanoid NAO in complicated workspaces, which is hardly seen in the available literature. Inspection in simulation and experimental workspaces verifies the robustness of the designed navigational controller. Considering minimum error ranges and near collaboration, the findings from both frameworks are evaluated against each other in respect of specified navigational variables. Finally, concerning other present approaches, the designed controller is also examined, and major modifications in efficiency have been reported.

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A Fuzzy Logic-Based Control Algorithm for the Recharge/V2G of a Nine-Phase Integrated On-Board Battery Charger

Electronics ◽

10.3390/electronics9060946 ◽

2020 ◽

Vol 9 (6) ◽

pp. 946 ◽

Cited By ~ 1

Author(s):

Felice De Luca ◽

Vito Calderaro ◽

Vincenzo Galdi

Keyword(s):

Fuzzy Logic ◽

Control Strategy ◽

Energy Demand ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Energy Resource ◽

Reference Value ◽

Active Role ◽

Effective Control ◽

Battery Charger

Energy demand associated with the ever-increasing penetration of electric vehicles on worldwide roads is set to rise exponentially in the coming years. The fact that more and more vehicles will be connected to the electricity network will offer greater advantages to the network operators, as the presence of an on-board battery of discrete capacity will be able to support a whole series of ancillary services or smart energy management. To allow this, the vehicle must be equipped with a bidirectional full power charger, which will allow not only recharging but also the supply of energy to the network, playing an active role as a distributed energy resource. To manage recharge and vehicle-to-grid (V2G) operations, the charger has to be more complex and has to require a fast and effective control structure. In this work, we present a control strategy for an integrated on-board battery charger with a nine-phase electric machine. The control scheme integrates a fuzzy logic controller within a voltage-oriented control strategy. The control has been implemented and simulated in Simulink. The results show how the voltage on the DC-bus is controlled to the reference value by the fuzzy controller and how the CC/CV charging mode of the battery is possible, using different charging/discharging current levels. This allows both three-phase fast charge and V2G operations with fast control response time, without causing relevant distortion grid-side (Total Harmonic Distortion is maintained around 3%), even in the presence of imbalances of the machine, and with very low ripple stress on the battery current/voltage.

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