Autonomous Mobile Robot Control Using Fuzzy Logic and Genetic Algorithm

Narvydas, Gintautas; Simutis, Rimvydas; Raudonis, Vidas

doi:10.1109/idaacs.2007.4488460

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…Narvydas, Simutis, and Raudonis proposed FUZZY logic, IF-THEN rules, and Genetic Algorithm for autonomous mobile robot control. Their experiment showed that the control using fuzzy logic gives best results than the other control which uses IF-THEN rules because there are no motors speed jumps [16].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic Controller

Hussein¹,

Al-Mutairi²

2019

IJCSIT

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Autonomous mobile robots have been used to carry out different tasks without continuous human guidance. To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them. Navigation means that the robot can move through the environment to reach a destination. Obstacles avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles position around them and store their positions in shared memory. By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic Controller

Hussein¹,

Al-Mutairi²

2019

IJCSIT

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“…Transitions between these states are determined by current sensor readings, whether or not any obstacle has been detected within the sensors' range. This algorithm has been called the Rule Set [7]. An illustration depicting its functioning is presented in Figure 14.…”

Section: Design Of Control Algorithmsmentioning

confidence: 99%

Design of Control Algorithms for Mobile Robots in an Environment with Static and Dynamic Obstacles

Piotrowski¹,

Maciąg²,

Makohoń³

et al. 2019

JAMRIS

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This article proposes the construction of autonomous mobile robots and designing of obstacle avoidance algorithms for them. Nowadays, mobile robots are gaining more and more popularity on the customer as well as industrial market, for example as automatic vacuum cleaners or lawnmowers. Obstacle avoidance algorithms play an important role in performance of this types of robots. The proposed algorithms were designed for builds with rather not expensive electronic components, especially sensors with limited precision and dynamics. The project began with the selection of needed parts and building materials as well as designing of the PCB and assembling the whole construction. The project included also designing and developing the software responsible for, among others, implementation of obstacle avoidance algorithms. After the project's completion, a series of tests in a closed environment was conducted to verify the quality of vehicles' performance. Results of tests were positive.

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“…In additional to fuzzy logic control, genetic algorithm and neural network have been used to improve the control scheme. Fuzzy logic control and genetic algorithm are also used in [17] to find the optimal parameters for the fuzzy logic system. Four Fuzzy Logic Controllers are used to navigate the mobile robot in [18].…”

Section: B Mobile Robot Navigationmentioning

confidence: 99%

Smart mobile robot for security of low visibility environment

Faisal

Mathkour

Alsulaiman

2015

2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW)

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The field of robotics has apparently appeared to replace jobs that humans usually dislike to do, jobs that have low tolerance for faults and risks or jobs for which human resource is not available. For these reasons, many types of robot have appeared, such as man-like robots, car-like robots and mobile robots. Mobile robots have become a target for many applications, such as security patrolling , manufacturing, materials handling in the warehouse, and many others applications. In the last decade, mobile robots have become an important topic in the security field. Several methods have been introduced to deal with mobile robot and security. Nevertheless, most of these methods are not smart enough to work in low visibility environments. In this paper, we are going to introduce a novel smart security system, which uses a mobile robot as a security patrol in dark environment. The proposed system is a non-vision based system and designed to deal with the dark environments using artificial intelligence methods (Neural Network NN and Fuzzy Logic FL). The proposed system includes a motion control model, which is responsible for the navigation operation in the monitoring area. The NN and FL techniques are used to smartly determine the abnormal situation in the monitoring area. Many levels of alarms are used to tackle the deferent level of dangers.

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Autonomous Mobile Robot Control Using Fuzzy Logic and Genetic Algorithm

Cited by 17 publications

References 11 publications

A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic Controller

A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic Controller

Design of Control Algorithms for Mobile Robots in an Environment with Static and Dynamic Obstacles

Smart mobile robot for security of low visibility environment

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