scholarly journals Navigation of differential drive mobile robot on predefined, software designed path

2016 ◽  
Vol 3 (1-2.) ◽  
Author(s):  
Áron Papp ◽  
László Szilassy ◽  
József Sárosi
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Sensor Data ◽  
Rule Base ◽  
Servo Motor ◽  
Differential Drive ◽  
Navigation Data ◽  
Position And Orientation ◽  
Input Variables ◽  
Robot Position

This paper will be presenting the process of mobile robot movement controlling, from the task of collecting sensor data until the problem of controlling data to the servo motor controllers. In details, the first part will show the mechanism of converting CAD data to routes, and the processing of the navigation data read from the sensors and calculated from former controlling commands. The second part will explain the processing of navigation data, the applying of the actual robot position and orientation on the predefined virtual path and the production of the controller's input variables. The Fuzzy controller and the rule base will be introduced in the third part.

Implementation of a hexapod mobile robot with a fuzzy controller

Robotica ◽  
2005 ◽  
Vol 23 (6) ◽  
pp. 681-688 ◽  
Author(s):  
Makoto Kern ◽  
Peng-Yung Woo
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Fuzzy Rule ◽  
Sensor Data ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Different Types ◽  
Autonomous Robot Navigation

Fuzzy logic has features that are particular attractive in light of the problems posed by autonomous robot navigation. Fuzzy logic allows us to model different types of uncertainty and imprecision. In this paper, the implementation of a hexapod mobile robot with a fuzzy controller navigating in unknown environments is presented. The robot, MKIII, interprets input sensor data through the comparison of values in its fuzzy rule base and moves accordingly to avoid obstacles. Results of trial run experiments are presented.

scholarly journals Using Fuzzy Logic to Adjust Plasma Concentration Based on a Two-Compartment Pharmacokinetic Model

2013 ◽  
Vol 341-342 ◽  
pp. 1171-1174
Author(s):  
Lei Zhao ◽  
Xin Ling Shi ◽  
Yu Feng Zhang ◽  
Ya Jie Liu
Keyword(s):  
Fuzzy Logic ◽  
Pharmacokinetic Model ◽  
Fuzzy Controller ◽  
Reference Value ◽  
Rule Base ◽  
Loop Control ◽  
Closed Loop Control System ◽  
Loop Control System ◽  
Input Variables ◽  
Adaptation Scheme

In this paper, a closed-loop control system was developed using fuzzy logic to adapt the parameters of a pharmacokinetic (PK) model. The system is based on a two-compartment PK model with first-order rate process of oral administration. The fuzzy logic adaptation scheme uses the error and the change in error as the input variables. The output variable of the fuzzy controller is the scaling factor to adjust the PK parameter. The fuzzy controller adjusted the real situation concentration to the reference value derived from the parameters of population mean data according to the established rule-base. The simulation results show that the controller provides good performance to adjust the concentration with the reference values.

Fault Diagnosis and Reconfiguration for Mobile Robot Localization Based on Multi-Sensors Data Fusion

2021 ◽  
pp. 1-23
Author(s):  
Linda Hachemi ◽  
Mohamed Guiatni ◽  
Abedlkrim Nemra
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Mobile Robot ◽  
Fault Tolerant ◽  
Sensor Data ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Estimation System ◽  
And Performance ◽  
Robot Position

In this paper, we propose a new approach for fault tolerant localization using multi-sensors data fusion for a unicycle-type mobile robot. The main contribution of this paper is a new architecture proposal for fault diagnosis and reconfiguration for mobile robot localization using multi-sensors data fusion and the duplication/comparison approach. Four different sensors usually embedded in mobile robots (Camera, IMU, GPS, and Odometer) are considered, while six different sensors couples combinations are used for sensor data fusion and the duplication of the localization and estimation system. In order to reach this aim, three different filters (EKF, SVSF, and ASVSF) have been proposed and compared. For each selected filter, a comparison mechanism is then introduced to compute different residuals by comparing the estimated robot position for each sensor couples separately. Faults are then detected using the structural residual diagnosis method. This approach assumes the occurrence of a single fault at a given time. A reconfiguration mechanism is then applied by selected the healthy sensors couple and their corresponding fusion filter. Several scenarios are considered for navigation-based fault tolerant localization approaches. Simulation results are presented to illustrate the advantage and performance of the proposed architecture. The proposed solutions are implemented and validated successfully using the V-REP simulator.

Design of Analog Fuzzy Controller for Autonomous Mobile Robot

2017 ◽  
Vol 25 (03) ◽  
pp. 477-494 ◽  
Author(s):  
Vinod Kapse ◽  
Bhavana Jharia ◽  
S. S. Thakur ◽  
C. P. Gupta
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Local Environment ◽  
Cmos Technology ◽  
Rule Base ◽  
Autonomous Mobile Robot ◽  
Single Output ◽  
Maximum Delay ◽  
Rule Change ◽  
Intelligent Controllers

The analog fuzzy intelligent controllers for autonomous mobile robot to avoid static and dynamic obstacles in its local environment are presented. The controller designed for the robot is reconfigurable in nature in terms of number of rules in database i.e. flexibility for online rule change as per the frequency of obstacle in the local environment. The controller is proposed with adjustable membership function in terms of shape and degree of overlapping with dynamic rule base. New accurate MAX and MIN circuits are introduced. The controller is simulated using Tanner® tool. The two-input single-output fuzzy controller with 25 rules is implemented in 0.25µm CMOS technology. The maximum delay was found to be 9.915ns for the processing of 25 rules and the value of FLIPS was found to be 100.85 MFLIPS.

Fuzzy Reasoning in Mobile Robots Control

2010 ◽  
Vol 166-167 ◽  
pp. 191-196
Author(s):  
Adrian Dumitriu
Keyword(s):  
Fuzzy Logic ◽  
Data Integration ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Fuzzy Controller ◽  
Fuzzy Reasoning ◽  
Sensor Data ◽  
Rough Terrain ◽  
Acceleration Sensors ◽  
Mems Technology

The paper presents some author’s experiments carried out within the frame of a research project and destined to endow mobile robot modules with small and simple sensors to support navigation. Range sensors, proximity sensors and acceleration sensors in MEMS technology were used and Fuzzy logic has proved to be an adequate tool for sensor data integration. A Fuzzy controller has been developed and tested on a mobile robot moving on rough terrain.

scholarly journals A simple goal seeking navigation method for a mobile robot using human sense, fuzzy logic and reinforcement learning

2008 ◽  
Vol 18 (1) ◽  
pp. 23-27 ◽  
Author(s):  
Hamid Boubertakh ◽  
Mohamed Tadjine ◽  
Pierre-Yves Glorennec ◽  
Salim Labiod
Keyword(s):  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Industrial Applications ◽  
Rule Base ◽  
Human Sense ◽  
Navigation Method

This paper proposes a new fuzzy logic-based navigation method for a mobile robot moving in an unknown environment. This method allows the robot obstacles avoidance and goal seeking without being stuck in local minima. A simple Fuzzy controller is constructed based on the human sense and a fuzzy reinforcement learning algorithm is used to fine tune the fuzzy rule base parameters. The advantages of the proposed method are its simplicity, its easy implementation for industrial applications, and the robot joins its objective despite the environment complexity. Some simulation results of the proposed method and a comparison with previous works are provided.

Co-Evolution of Fuzzy Controller for the Mobile Robot Control

2004 ◽  
Vol 8 (4) ◽  
pp. 356-361 ◽  
Author(s):  
Kwang-Sub Byun ◽  
◽  
Chang-Hyun Park ◽  
Kwee-Bo Sim
Keyword(s):  
Mobile Robot ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Membership Functions ◽  
Mobile Robot Control ◽  
Evolution Algorithms

In this paper, we design the fuzzy rules using a modified Nash Genetic Algorithm. Fuzzy rules consist of antecedents and consequents. Because this paper uses the simplified method of Sugeno for the fuzzy inference engine, consequents have not membership functions but constants. Therefore, each fuzzy rule in this paper consists of a membership function in the antecedent and a constant value in the consequent. The main problem in fuzzy systems is how to design the fuzzy rule base. Modified Nash GA coevolves membership functions and parameters in consequents of fuzzy rules. We demonstrate this co-evolutionary algorithm and apply to the design of the fuzzy controller for a mobile robot. From the result of simulation, we compare modified Nash GA with the other co-evolution algorithms and verify the efficacy of this algorithm.

scholarly journals Fuzzy Coordinated Control of an Electric Drive System of a Four-Wheel Mobile Robot

2020 ◽  
Vol 23 (3) ◽  
pp. 61-69
Author(s):  
E. O. Meshkovskiy ◽  
◽  
A. D. Kurmashev ◽  
V. Ya. Frolov ◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Electric Drive ◽  
Computer Experiments ◽  
Coordinated Control ◽  
Drive System ◽  
Rule Base ◽  
Differential Drive ◽  
Electric Drive System ◽  
System Controller ◽  
The Relationship

This paper presents the construction of a fuzzy system controller of a coordinated control system of an electric drive system for a four-wheel mobile robot with two differential drive units. The structure of this system regulator, the rule base, and the expression of the relationship between its elements are shown. In the article the authors show the results of computer experiments in graphs of trajectory error for different configurations of the robot and the system controller coefficient values.

Orientation (Yaw) Fuzzy Controller Applied to a Differential Drive Mobile Robot Prototype for Indoor and Outdoor Applications

2015 ◽  
Author(s):  
Renan Moreira Pinto ◽  
Andres Eduardo Baquero Velasquez ◽  
Henry Borrero Guerrero ◽  
Vitor Akihiro Hisano Higuti ◽  
Daniel Varela Magalhães ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Differential Drive ◽  
Indoor And Outdoor
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