Fuzzy Target Tracking Control of Autonomous Mobile Robots by Using Infrared Sensors

Li, T.-H.S.; Chang, Shih-Jie; Tong, Wei

doi:10.1109/tfuzz.2004.832526

Cited by 160 publications

(49 citation statements)

References 32 publications

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“…The developed fuzzy controllers have been implemented real-time using field-programmable gate array (FPGA) chip, and tested it in various experimental scenarios. Li & Chang [48] have presented a real-time fuzzy target tracking control scheme for autonomous mobile robots using infrared sensors. The behavior-based fuzzy logic controller has been made by Dongshu et al [49] to solve the navigation problem of mobile robot in unknown dynamic environment.…”

Section: Hybridization Of Fuzzy and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

163

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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

163

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“…q is the number of premise variables and 1 ,, q xx are the premise variables. The fuzzy system is inferred as follows (Chen et al, 1999;Chen et al, 2000;Li et al, 2004;Lian et al, 2001;Takagi and Sugeno, 1985) …”

Section: Minimax Robust Synthetic Gene Network Via Fuzzy Interpolatimentioning

confidence: 99%

“…Since the synthetic gene networks are highly nonlinear, it is not easy to solve the robust synthetic gene network design problem directly by the nonlinear dynamic game method directly. Recently, fuzzy systems have been employed to efficiently approximate nonlinear dynamic systems to solve the nonlinear control problem (Chen et al, 1999;Chen et al, 2000;Hwang, 2004;Li et al, 2004;Lian et al, 2001;Takagi & Sugeno, 1985). A TakagiSugeno (T-S) fuzzy model (Takagi & Sugeno, 1985) is proposed to interpolate several linearized genetic networks at different operating points to approximate the nonlinear gene network via some smooth fuzzy membership functions.…”

mentioning

confidence: 99%

Stochastic Game Theory Approach to Robust Synthetic Gene Network Design

Chen¹,

Li²,

Tu³

2010

Game Theory

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“…Genetic learning fuzzy logic control rules to capture the target are discussed in [11]. In [9], fuzzy tracking control of a target using a mobile robot is achieved using infrared sensors, where a fuzzy sliding mode control scheme is suggested to accomplish the control task. Methods based on fuzzy control approaches may suffer from the following drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Line of sight robot navigation toward a moving goal

Belkhouche

Rastgoufard

2006

IEEE Trans. Syst., Man, Cybern. B

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Abstract-In this paper, we consider the problem of robot tracking and navigation toward a moving goal. The goal's maneuvers are not a priori known to the robot. Thus, off-line strategies are not effective. To model the robot and the goal, we use geometric rules combined with kinematics equations expressed in a polar representation. The intent of the strategy is to keep the robot between a reference point, called the observer, and the goal. We prove under certain assumptions that the robot navigating using this strategy reaches the moving goal successfully. In the presence of obstacles, the method is combined with an obstacle avoidance algorithm. The robot then moves in two modes, the navigation mode and the obstacle avoidance mode. Simulation of various scenarios highlights the efficiency of the method and provides an instructive comparison between the paths obtained for different reference points.Index Terms-Line of sight guidance law, relative kinematics equations, robotic navigation toward a moving goal, tracking.

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Fuzzy Target Tracking Control of Autonomous Mobile Robots by Using Infrared Sensors

Cited by 160 publications

References 32 publications

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Stochastic Game Theory Approach to Robust Synthetic Gene Network Design

Line of sight robot navigation toward a moving goal

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