Evolutionary Robot Wall-Following Control Using Type-2 Fuzzy Controller With Species-DE-Activated Continuous ACO

Hsu, Chia-Hung; Juang, Chia‐Feng

doi:10.1109/tfuzz.2012.2202665

Cited by 113 publications

(54 citation statements)

References 31 publications

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“…Hsu & Juang [145] have designed the wall-following mobile robot using a type-2 fuzzy controller (IT2FC) and integrated it with an ACO algorithm to improve the performance of the controller. The steering angle and moving speed of the wall-following mobile robot has been controlled by two type-2 fuzzy controllers.…”

Section: Ant Colony Optimization Algorithm and Other Nondeterministicmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

184

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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: Ant Colony Optimization Algorithm and Other Nondeterministicmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

184

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“…In order to improve noise resistance ability, some researchers applied control laws based on interval type-2 fuzzy logic [10][11][12][13]. Hsu et al, [10] proposed an evolutionary wall-following control of a mobile robot using an interval type-2 fuzzy controller with Species-DifferentialEvolution activated Continuous Ant Colony Optimization (SDE-CACO). All fuzzy rules are generated online using a clusteringbased approach during the evolutionary learning process.…”

Section: Introductionmentioning

confidence: 99%

Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors

Al-Mutib¹,

Abdessemed²

2017

Adv. sci. technol. eng. syst. j.

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“…The basic idea is to split the heuristic search into two stages: preprocess stage and path planning stage. Hsu and Juang [27] proposed an evolutionary wall-following control of a mobile robot using an interval type-2 fuzzy controller(IT2FC) with species-differential-evolution-activated continuous ant colony optimization(SDE-CACO). The proposed SDE-CACO is compared with various population-based optimization algorithms to demonstrate its efficiency and effectiveness in the wall-following control problem.…”

Section: Introductionmentioning

confidence: 99%