Fuzzy logic controllers design for omnidirectional mobile robot navigation

Masmoudi, Mohamed; Krichen, Najla; Derbel, Nabil

doi:10.1016/j.asoc.2016.08.057

Cited by 89 publications

(47 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Next indicators were the absolute and the quadratic regulation area. In our case, these areas were delimited by the calculated path of the mobile robot and the connecting line between the starting and the target point [1,6].…”

Section: Methodsmentioning

confidence: 99%

“…Currently, one of the most demanding areas of robotics is the control of mobile robots in a defined environment with a certain degree of autonomy [1]. The main challenge of the autonomous navigation of mobile robots is the quickest possible and the most precise reaching of the target position.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers used fuzzy logic to investigate its behaviour and characteristics in the field of controlling autonomous mobile robots [1,3,4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot

et al. 2019

View full text Add to dashboard Cite

The application of intelligent control algorithms in the field of autonomous mobile robotics enables effective control of mobile robots with a minimal possible error. At present, most of commonly used systems to control an autonomous mobile robot are, however, too complicated to design. Our goal was to design a fuzzy controller with an optimal number of inference rules in a way to achieve the best possible level of quality of mobile robot control. The proposed controller was implemented in the mobile robot EN20, where the time of regulation and the absolute and the quadratic control surface were used to evaluate quality parameters. The analysis of the quality of control was performed with the use of a fuzzy controller with 9, 25 and 49 inference rules. We found from the results of modelling that the greatest influence on the quality of control of a mathematical model of the mobile robot had the number of inference rules of the fuzzy controller. Mathematical and graphical dependence of the quality of control on the number of inference rules was calculated from the parameters of the quality of control. The results of the research are equations of the curves of the individual parameters of control of quality, which show that for the control of the autonomous mobile robot EN 20, that the optimal fuzzy controller has 49 inference rules, triangular functions of the pertinence of individual linguistic values and it is defuzzificated by Centroid method.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot

et al. 2019

View full text Add to dashboard Cite

show abstract

“…It plays an important role in many fields such as industry, agriculture, service, medicine. At present, the study of robot autonomous navigation tends to develop intelligently, and some navigation algorithms have been proposed, for example neural network [1,2], fuzzy control [3,4] and the combination of fuzzy control and neural network [5]. The neural network has a strong ability to learn and train, but lacks processing and description capabilities of fuzzy information.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Navigation System of Hexapod Robot based on Fuzzy Neural Network

Li¹,

Kong²,

Du³

2016

Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications

View full text Add to dashboard Cite

A closed-loop autonomous navigation system based on fuzzy neural network is proposed to deal with the autonomous navigation issue of hexapod robot in unknown environment. The closed-loop system is designed in order to optimize the output performance. The navigation algorithm is designed to combine fuzzy control with neural network. Fuzzy control is used to realize the ability of logical reasoning and neural network is conducive to learning and training ability. The ambient sensors are a GPS sensor, an electronic compass sensor and an ultrasonic sensor with sector scanning state. These sensors can complete the detection of the surrounding obstacles, the target course angle and the current course angle. The performance of the robot's autonomous navigation system is compared with the open-loop and closed-loop system based on fuzzy neural network in the simulation experiment, which demonstrates that walking time based on closed-loop system significantly declines compared to the open-loop system, meanwhile, the traveling speed also improves. In the way to the destination, the robot can safely and quickly bypass the obstacles without any redundant paths. Kinematics Model AnalysisEnvironmental information acquisition and optimization. The mechanical structure of the hexapod robot is shown in Fig.1. The robot's perception of the surrounding environment is realized by sensors, therefore the types and distribution of sensors are very important for the whole system. This paper selects ultrasonic sensor, GPS sensor and electronic compass sensor as the perceptrons. In order to solve the problem that ultrasonic sensor can not accurately obtain the boundary

show abstract

“…The applications of fuzzy systems today are of great relevance in the control of robotic agents, for example, in displacement control applications [3] [4], learning reinforcement for navigation [5] and even for collaborative work among robotic agents [6]. But a robot to navigate and interact with its environment, requires a machine vision system to identify patterns as objects in the scene.…”

mentioning

confidence: 99%

RGB-D training for convolutional neural network with final fuzzy layer for depth weighting

Moreno¹,

Sánchez²,

Ovalle³

2017

ces

View full text Add to dashboard Cite

This paper presents training of novel hybrid network based on three deep convolutional neural network architectures applied to object recognition, based on the depth information supplied for a RGBD camera. For this case, the depth information allows to set the dataset of training images of each network, its architecture and its characteristics, generating a dynamic recognition application by variation of the image capture point, whose final layer is determined by a diffuse inference system. The general architecture designed allows an efficient object recognition applicable to robotic mobile agents, whose perspective of the object varies when approaching or moving away from them, showing an overall performance of 90.19%.

show abstract

Fuzzy logic controllers design for omnidirectional mobile robot navigation

Cited by 89 publications

References 1 publication

The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot

The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot

Autonomous Navigation System of Hexapod Robot based on Fuzzy Neural Network

RGB-D training for convolutional neural network with final fuzzy layer for depth weighting

Contact Info

Product

Resources

About