Fuzzy logic based collision avoidance for a mobile robot

Robotica ◽  
1994 ◽  
Vol 12 (6) ◽  
pp. 521-527 ◽  
Author(s):  
Angelo Martinez ◽  
Eddie Tunstel ◽  
Mo Jamshidi
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Collision Avoidance ◽  
Intelligent Control ◽  
Indoor Environment ◽  
Mobile Robotics ◽  
Fuzzy Controller ◽  
Approximate Reasoning ◽  
Robot System ◽  
Avoidance Problem

SummaryNavigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In general, the problem consists of achieving sensor based motion control of a mobile robot among obstacles in structured and/or unstructured environments with collision-free motion as the priority. A fuzzy logic based intelligent control strategy has been developed here to computationally implement the approximate reasoning necessary for handling the uncertainty inherent in the collision avoidance problem. The fuzzy controller was tested on a mobile robot system in an indoor environment and found to perform satisfactorily despite having crude sensors and minimal sensory feedback.

Intelligent Control Concerning Obstacle Avoidance of Mobile Robot

1996 ◽  
Vol 8 (1) ◽  
pp. 104-111
Author(s):  
Hideo Nagata ◽  
◽  
Takeshi Tsuchiya ◽  
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Computer Simulations ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Two Stage ◽  
Human Operators

This paper describes a collision avoidance method for a mobile robot operated by the operational histories of human operators as well as by a computerized fuzzy controller. In order to cope with a changeable environment, a mobile robot has to provide higher planning which watches for the success or failure of a plan and provides feedback of perception. in this paper,a mobile robot makes a judgment based on relative positions and speeds between the robot and obstacles, and predicts a next environment to some extent. Moreover, the mobile robot tries to acquire skill-based intelligence through a layered neural network. Some results of computer simulations are shown to ascertain the effectiveness of this two-stage control. Finally, we propose layered pathplanning in terms of thinking levels which take into consideration the dynamics of a robot.

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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.

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/”.

Research on NN-PID Motion Control Technology for a Wheeled Mobile Robot

2012 ◽  
Vol 538-541 ◽  
pp. 2636-2640
Author(s):  
Shi Zhu Feng ◽  
Ming Xu
Keyword(s):  
Mobile Robot ◽  
Motion Control ◽  
Pid Controller ◽  
Mobile Robotics ◽  
Kinematic Model ◽  
Wheeled Mobile Robot ◽  
Control Technology ◽  
Robot System

Robotics is a spiry integral technology of mechanics, electrics and cybernetics. Through systematical study of a wheeled mobile robot, The kinematic model of it is deduced. A Cerebella Model Articulation Controller (CMAC) PID controller was developed to control the motion to accomplish the realistic motions of the wheeled mobile robot system. The experimental is carried out. The results prove the algorithm is correct, and indicate that the design of CMAC-PID controller is a success. The whole research will provide a reference to the study of the mobile robotics.

Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling

2016 ◽  
Vol 36 (3) ◽  
pp. 318-332 ◽  
Author(s):  
Zhenyu Wu ◽  
Guang Hu ◽  
Lin Feng ◽  
Jiping Wu ◽  
Shenglan Liu
Keyword(s):  
Mobile Robot ◽  
Collision Avoidance ◽  
Potential Field ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Artificial Potential Field ◽  
Content Type ◽  
Negative Pole ◽  
Avoidance Problem ◽  
Three Dimensional Space

Purpose This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM). Design/methodology/approach The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment. Findings Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close. Originality/value As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.

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.

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