Fuzzy logic controller for mobile robot navigation to avoid dynamic and static obstacles

Boujelben, Maissa; Ayedi, Dorra; Rekik, Chokri; Derbel, Nabil

doi:10.1109/ssd.2017.8166963

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…In order to improve the performance of WFR, the closed-loop controller, FLC is used. Essentially, FLC processes the input membership function and makes decision with respect to the proper output based on the rulebased system [3,4,15,16,17,19,26,27]. There have been many products applying the principle of FLC, such as in the case of adjusting the air conditioner based on the actual temperature, pressing the pedal of an automatic car based on the crowds around, and adjusting the heat based on the food maturity placed on the barbeque cooker.…”

Section: Methodsmentioning

confidence: 99%

An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

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The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

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Section: Methodsmentioning

confidence: 99%

An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

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“…Based on this scheme, the sensors initially provide information about the distance between the robot and the obstacles located in its operation area; also, the signals for the speed of each actuator are read. Later, this information gets into electronic input and output interface modules (Arduino Mega sensor shield v2.0 [7]). This process uses control signals generated by PC and transmitted through the serial port to the electronic input and output interface modules.…”

Section: Methodsmentioning

confidence: 99%

Application of combination of neural networks and fuzzy logic in planning of mobile robot motion in unknown environment

Nemeiksis

Osadčuks

2019

Engineering for Rural Development

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The significance of automated control systems to contemporary society is profound, as large-scale industrial robots are used in industrial plants, autonomous vacuum cleaners are applied at home, and autonomous cars are driven in the streets. Regardless of the nature of the robot, one of its main tasks is to do no harm to the person and the environment, and that is the greatest problem of robotics-orientation. Therefore, the problem of orientation of autonomous robots is being solved by a robot exploring the environment, remembering the space and time-varying environmental changes. On that ground, the task of this article is to develop such scientific researches, which define the mobile robot motion in an unknown environment, based on fuzzy logic and the analysis of neural networks, setting an aim to work out methods for developing intellectual systems for planning a mobile robot motion fuzzy logic and neural networks to ensure that the robot performs the planned and adjusted on the way safe trajectory in an environment with unknown obstacles. Therefore, the entire study in the article is aimed at the analysis of fuzzy logic that is analysed as the entirety of the mathematical description methods of fuzzy sets with the formalization of logical conclusions from the fuzzy assumptions, as in this case the decision-making mechanism always allows the generation of the robot's responsive motions caused by the appearance of obstacles in its trajectory; as well as the analysis of neural networks, which links between neurons determine the complexity and flexibility of the operation of the entire neural network, by addressing the problem of planning a mobile robot motion in an unknown environment and which basically depends upon the level of specific network training. As a result of the study, the method of a mobile robot motion in an unknown dynamic environment was created by using a multi-agent system, involving a combination of neural networks and fuzzy blocks.

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“…Illeperuma et al [22] presented a fuzzy logic to decide the locomotion of the mobile robot, where as the optical flow is used to get a vector of information from video scene to take a decision of robot locomotion by fuzzy logic. Boujelben et al [23] proposed a single-input single-output fuzzy controller to generate a smooth path so as to reach the target without colliding with static or dynamic obstacles using a single input and single output to reduce the fuzzy rules and the computational time.The work used a minimum number of regulations.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Assessment and Review of the Reactive Mobile Robot Navigation

Yosif¹,

Mahmood²,

Al-Khayyt³

2021

(AREJ)

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Nowadays, the mobile robot can be seen in different fields of engineering and science. The mobile robot can do some tasks that are so difficult or very risky to be performed by a human. Most of the works currently focus on implementing artificially intelligent algorithms and other algorithms that depend on the behaviour of nature. These approaches have been used in mobile robot navigation along uncertain manner. Mobile robot navigation strategies can be divided into two approaches: the classical approach and reactive approach. The classical approach related to static environment, whiles the reactive navigation is based on an unstructured environment. Path planning is one of the most important parts of the navigation system. In this paper, review and assessment of path planning strategies that can concern with the reactive approach are discussed, because it deal with the problem of dynamic environment. Numerous reactive methods have been introduced. Most of these presented works were concerned with simulation and a few of them have shown experimental implementation. Many papers tried to make a combination between two algorithms or more to increase the efficiency. It is concluded that reactive algorithms need more learning phases, complex in design, and require large memory storage.

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Fuzzy logic controller for mobile robot navigation to avoid dynamic and static obstacles

Cited by 11 publications

References 11 publications

An FLC-Pso Algorithm-Controlled Mobile Robot

An FLC-Pso Algorithm-Controlled Mobile Robot

Application of combination of neural networks and fuzzy logic in planning of mobile robot motion in unknown environment

Assessment and Review of the Reactive Mobile Robot Navigation

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