Fuzzy Logic Based Navigation of Mobile Robots

Yahmedi, Amur S. Al; Fatmi, Muhammed A.

doi:10.5772/25621

Cited by 23 publications

(14 citation statements)

References 20 publications

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“…Simulations have been conducted to evaluate the efficiency of the BBFM compared to two other popular architectures including the MOASM [17] and CDB [10]. The MOASM uses multi-objective optimization for command fusion.…”

Section: Simulationsmentioning

confidence: 99%

Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization

Nguyen¹,

Phung²,

Trần³

2017

IJCA

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Section: Simulationsmentioning

confidence: 99%

Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization

Nguyen¹,

Phung²,

Trần³

2017

IJCA

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“…В работе [5] рассматривается нечеткая модель перемещения мобильного робота в неопределенной среде. Роботу ставится задача управления переме-щением по заданной траектории согласно некоторой функции ( ) f x .…”

Section: литературный обзорunclassified

“…Особое внимание в данной работе уделяется пове-дению мобильного робота при преодолении препят-ствий различного рода, в том числе выходу из тупиков. Данный метод [5] хорошо работает для лабиринтов, однако, поскольку мобильный робот заранее не знает местонахождения и размеров препятствий, оптими-зация траектории робота также не представляется возможной.…”

Section: литературный обзорunclassified

Extension of methods of intelligent control of complex objects

Кучеренко

Дрюк

2014

EEJET

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“…In other words, this method can be used for robots with omnidirectional range sensors. An effective approach to navigate the wheeled mobile robots has been presented by Al Yahmedi and Fatmi [31]. Issues of individual behavior design and action coordination of the behaviors were addressed using fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Robot Motion Planning in an Unknown Environment with Danger Space

et al. 2019

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This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods.

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Fuzzy Logic Based Navigation of Mobile Robots

Cited by 23 publications

References 20 publications

Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization

Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization

Extension of methods of intelligent control of complex objects

Robot Motion Planning in an Unknown Environment with Danger Space

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