A fuzzy-based reactive controller for a non-holonomic mobile robot

Abdessemed, Foudil; Benmahammed, K.; Monacelli, Éric

doi:10.1016/j.robot.2004.02.006

Cited by 108 publications

(54 citation statements)

References 17 publications

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“…Fuzzy PWM (Pulse Width Modulation) controller has been presented in the article [41] for mobile robot navigation and obstacle avoidance in an unknown environment. Abdessemed et al [42] have designed an evolutionary algorithm to optimize the antecedent and consequent parameters of the fuzzy controller, and implemented it for mobile robot path planning. Selekwa et al [43] have presented the fuzzy behavior controller for mobile robot navigation in the densely obstacle populated environments.…”

Section: Hybridization Of Fuzzy and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

178

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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Section: Hybridization Of Fuzzy and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

178

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“…The use of the above algorithms for path finding for mobile robot requires more time and the finding of this path will not completely feasible for real-time movement. There are many fuzzy logic methods using various implementations or in combination with other techniques [10][11][12][13][14]. Mobile robot path planning based on neural network approaches presented by many researchers [15][16][17][18].Among the intelligent techniques ANFIS is a hybrid model which combines the adaptability capability of artificial neural network and knowledge representation of fuzzy inference system [19].Song and Sheen [20] developed a pattern recognition method based on fuzzy-neuro network for reactive navigation of a car-like robot.…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Approach for Mobile Robot Navigation

Mohanty

Parhi

2013

Lecture Notes in Computer Science

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Abstract. In recent times computational intelligent techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable optimization methods for modeling an engineering system. In this paper an efficient hybrid technique has been applied for mobile robot navigation using multiple adaptive neuro-fuzzy inference system (MANFIS). ANFIS has taken the advantages of both fuzzy inference system and artificial neural network. First, we design an adaptive fuzzy controller with four input parameters, two types of output parameters and three parameters each. Next each adaptive fuzzy controller acts as a single Sugeno-Takagi type fuzzy inference system where inputs are the different sensor based information and output corresponds to the velocity of the mobile robot. The implementation of the proposed navigational controller is discussed via numerous simulation examples. It is found that such an adaptive neuro-fuzzy controller is successfully and quickly finding targets in an unknown or partially unknown environment.

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“…By the Frobenius theorem, if ∆ is the distribution spanned by the two vector fields of Σ, the system is holonomic if ∆ is involutive under Lie bracketing, a condition that is not satisfied by (1). Due to these challenges, the path following problem has been attacked by several researchers from many angles, ranging from classical control approaches (Altafini, 1999;Kamga & Rachid, 1997;Kanayama & Fahroo, 1997), to nonlinear control methodologies (Altafini, 2002;Egerstedt et al, 1998;Koh & Cho, 1994;Samson, 1995;Wit et al, 2004) to intelligent control strategies (Abdessemed et al, 2004;Antonelli et al, 2007;Baltes & Otte, 1999;Cao & Hall, 1998;Deliparaschos et al, 2007;El Hajjaji & Bentalba, 2003;Lee et al, 2003;Liu & Lewis, 1994;Maalouf et al, 2006;Moustris & Tzafestas, 2005;Rodriguez-Castano et al, 2000;Sanchez et al, 1997;Yang et al, 1998). Of course, boundaries often blend since various approaches are used simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Of course, boundaries often blend since various approaches are used simultaneously. Fuzzy logic path trackers have been used by several researchers (Abdessemed et al, 2004;Antonelli et al, 2007;Baltes & Otte, 1999;Cao & Hall, 1998;Deliparaschos et al, 2007;El Hajjaji & Bentalba, 2003;Jiangzhou et al, 1999;Lee et al, 2003;Liu & Lewis, 1994;Moustris & Tzafestas, 2011;Ollero et al, 1997;Raimondi & Ciancimino, 2008;Rodriguez-Castano et al, 2000;Sanchez et al, 1997) since fuzzy logic provides a more intuitive way for analysing and formulating the control actions, which bypasses most of the mathematical load needed to tackle such a highly nonlinear control problem. Furthermore, the fuzzy controller, which can be less complex in its implementation, is inherently robust to noise and parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%