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.

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.

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

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.

Neural network and fuzzy logic techniques based collision avoidance for a mobile robot

Robotica ◽  
1997 ◽  
Vol 15 (6) ◽  
pp. 627-632 ◽  
Author(s):  
Minglu Zhang ◽  
Shangxian Peng ◽  
Qinghao Meng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Perceptual Space ◽  
Space Partitioning ◽  
Reactive Navigation ◽  
3 Dimensional ◽  
Sensory Data ◽  
Cluttered Environment

This paper is concerned with a mobile robot reactive navigation in an unknown cluttered environment based on neural network and fuzzy logic. Reactive navigation is a mapping between sensory data and commands without planning. This article's task is to provide a steering command letting a mobile robot avoid a collision with obstacles. In this paper, the authors explain how to perform a currently perceptual space partitioning for a mobile robot by the use of an ART neural network, and then, how to build a 3-dimensional fuzzy controller for mobile robot reactive navigation. The results presented, whether experimented or simulation, show that our method is well adapted to this type of problem.

Navigation Control of a Mobile Robot under Time Constraint using Genetic Algorithms, CSP Techniques, and Fuzzy Logic

2016 ◽  
pp. 932-954
Author(s):  
Tlijani Hayet ◽  
Tlijani Hatem ◽  
Knani Jilani
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Time Delay ◽  
Mobile Robot ◽  
Constraint Satisfaction ◽  
Current Velocity ◽  
Fuzzy Controller ◽  
Time Window ◽  
Constraint Satisfaction Problem ◽  
Navigation Control

This chapter proposes a two-input-and-one-output fuzzy controller for a two-wheeled mobile robot. The robot moves following a hybrid plan based on Genetic Algorithms and Constraint Satisfaction Problem techniques. The path yielded by this hybrid plan is structured by artificial beacons. In each position of these beacons, the current velocity of the controlled robot is by itself an input for the fuzzy controller. The second input is the time delay between the current position and the next sub goal to be reached. The proposed fuzzy controller aims to converge the time delay between positions in the path tracking closely to a time window. It gives the appropriate acceleration so that the time delay becomes closer to the time window.

Robotic transportation

1990 ◽  
Vol 36 (9) ◽  
pp. 1544-1550 ◽  
Author(s):  
W S Lob
Keyword(s):  
Information Systems ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Test Equipment ◽  
Sensor Data ◽  
Intelligent Sensor ◽  
Hospital Environments ◽  
Extensive Facility ◽  
New Applications ◽  
User Friendly

Abstract Mobile robots perform fetch-and-carry tasks autonomously. An intelligent, sensor-equipped mobile robot does not require dedicated pathways or extensive facility modification. In the hospital, mobile robots can be used to carry specimens, pharmaceuticals, meals, etc. between supply centers, patient areas, and laboratories. The HelpMate (Transitions Research Corp.) mobile robot was developed specifically for hospital environments. To reach a desired destination, Help-Mate navigates with an on-board computer that continuously polls a suite of sensors, matches the sensor data against a pre-programmed map of the environment, and issues drive commands and path corrections. A sender operates the robot with a user-friendly menu that prompts for payload insertion and desired destination(s). Upon arrival at its selected destination, the robot prompts the recipient for a security code or physical key and awaits acknowledgement of payload removal. In the future, the integration of HelpMate with robot manipulators, test equipment, and central institutional information systems will open new applications in more localized areas and should help overcome difficulties in filling transport staff positions.

Selected Papers from HNICEM 2007

2008 ◽  
Vol 12 (4) ◽  
pp. 327-327
Author(s):  
Elmer P. Dadios ◽  
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Real Time ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Optimal Path ◽  
Image Processing Technique ◽  
Special Issue ◽  
International Conference ◽  
The Third

The Third International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) was held in Century Park Hotel, Manila, Philippines from March 15 to 18, 2007. The theme on this conference was Technology Creativity and Innovations for Economic Development. As has been done from the previous HNICEM conferences, cutting edge papers presented from this conference are reviewed and selected for JACIII special issue publication. In this special issue, 10 articles are selected that will provide valuable references for researchers and practitioners. The first article presents an integrated algorithm that provides a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment. The second article presents a fuzzy controller technique in navigation with obstacle avoidance for a general purpose mobile robot in a given global environment with image processing technique using Open Source Computer Vision. The third article presents a model-based controller for helicopter using the sliding mode approach. The controller assumes that only measured outputs are available and it uses sliding mode observer to estimate the state of the system. The fourth article presents a real-time fuzzy logic based parallel parking system in an FPGA platform. The fifth article presents performance analysis of container unloading operations using simplified analytical model (SAM). The sixth article presents a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm use to forecast electrical load. The seventh article presents a new design and implementation of a multi-output fuzzy controller for real time control which utilizes lesser memory and executes faster than an existing type of multiple single-output fuzzy logic controllers. The eight article presents a new method based on multi-objective evolutionary algorithms to evolve low complexity neural controllers that allows an agent to perform two different tasks simultaneously. The ninth and tenth articles present genetic networks programming for stock market trading rules and for traffic systems applications, respectively. We extend our warmest thanks and deepest gratitude to the distinguished authors and reviewers who have contributed to this special issue for their outstanding contributions and cooperation. We are also grateful to Prof. Toshio Fukuda and Prof. Kaoru Hirota, chief editors of JACIII, for their continued support to all the HINICEM International Conferences. Come March 12 to 15, 2009, the 4th HNICEM International Conference will be held in Manila, Philippines. We thank the IEEE Philippines for its continuing sponsorship. Also to JACIII journal, as outstanding papers presented in this conference will be selected for publication in a special issue. We invite you to submit your research papers and to participate in HNICEM 2009. For further information, please visit “http://www.dlsu.edu.ph/conferences/hnicem/”.

scholarly journals Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hajer Omrane ◽  
Mohamed Slim Masmoudi ◽  
Mohamed Masmoudi
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Mobile Robot Navigation ◽  
Infrared Range ◽  
Indoor Environments ◽  
Autonomous Mobile Robot ◽  
Ir Sensors ◽  
Navigation Algorithms ◽  
Trajectory Tracking Controller

This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.

scholarly journals Design Interval Type-2 Fuzzy Like (PID) Controller for Trajectory Tracking of Mobile Robot

2019 ◽  
pp. 1-15
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Pid Controller ◽  
Kinematic Model ◽  
Superior Performance ◽  
Fuzzy Like ◽  
Interval Type

One of the major problems in the field of mobile robots is the trajectory tracking problem. There are a big number of investigations for different control strategies that have been used to control the motion of the mobile robot when the nonlinear kinematic model of mobile robots was considered. The trajectory tracking control of autonomous wheeled mobile robot in a changing unstructured environment needs to take into account different types of uncertainties. Type-1 fuzzy logic sets present limitations in handling those uncertainties while type-2 fuzzy logic sets can manage these uncertainties to give a superior performance. This paper focuses on the design of interval type-2 fuzzy like proportional-integral-derivative (PID) controller for the kinematic model of mobile robot. The firefly optimization algorithm has been used to find the best values of controller’s parameters. The aim of this controller is trying to force the mobile robot tracking a pre-defined continuous path with minimum tracking error. The Matlab simulation results demonstrate the good performance and robustness of this controller. These were confirmed by the obtained values of the position tracking errors and a very smooth velocity, especially with regards to the presence of external disturbance or change in the initial position of mobile robot. Finally, in comparison with other proposed controllers, the results of nonlinear IT2FLC PID controller outperform the nonlinear PID neural controller in minimizing the MSE for all control variables and in the robustness measure.

scholarly journals Furniture Model Creation Through Direct Teaching to a Mobile Robot

2008 ◽  
Vol 20 (2) ◽  
pp. 213-220 ◽  
Author(s):  
Kimitoshi Yamazaki ◽  
◽  
Takashi Tsubouchi ◽  
Masahiro Tomono ◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Real Life ◽  
Experimental Results ◽  
Sensor Data ◽  
Small Object ◽  
Laser Range Finder ◽  
Range Finder ◽  
Laser Range ◽  
Direct Teaching

In this paper, a modeling method to handle furniture is proposed. Real-life environments are crowded with objects such as drawers and cabinets that, while easily dealt with by people, present mobile robots with problems. While it is to be hoped that robots will assist in multiple daily tasks such as putting objects in into drawers, the major problems lies in providing robots with knowledge about the environment efficiently and, if possible, autonomously.If mobile robots can handle these furniture autonomously, it is expected that multiple daily jobs, for example, storing a small object in a drawer, can be performed by the robots. However, it is a perplexing process to give several pieces of knowledge about the furniture to the robots manually. In our approach, by utilizing sensor data from a camera and a laser range finder which are combined with direct teaching, a handling model can be created not only how to handle the furniture but also an appearance and 3D shape. Experimental results show the effectiveness of our methods.

Continuous mobility of mobile robots with a special ability for overcoming driving failure on rough terrain

Robotica ◽  
2016 ◽  
Vol 35 (10) ◽  
pp. 2076-2096
Author(s):  
He Xu ◽  
X. Z. Gao ◽  
Yan Xu ◽  
Kaifeng Wang ◽  
Hongpeng Yu ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Driving Force ◽  
Failure Modes ◽  
Sliding Mode ◽  
Rough Terrain ◽  
Force Analysis ◽  
Uncertain Environments ◽  
Wheeled Mobile Robots ◽  
Robot Configuration

SUMMARYFor wheeled mobile robots moving in rough terrains or uncertain environments, driving failure will be encountered when trafficability failure occurs. Continuous mobility of mobile robots with special ability for overcoming driving failure on rough terrain has rarely been considered. This study was conducted using a four-wheel-steering and four-wheel-driving mobile robot equipped with a binocular visual system. First, quasi-static force analysis is carried out to understand the effects of different driving-failure modes on the mobile robot while moving on rough terrain. Secondly, to make the best of the rest of the driving force, robot configuration transformation is employed to select the optimal configuration that can overcome the driving failure. Thirdly, sliding mode control based on back-stepping is adopted to enable the robot achieve continuous trajectory tracking with visual feedback. Finally, the efficacy of the presented approach is verified by simulations and experiments.

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