Evolutionary Wall-Following Hexapod Robot Using Advanced Multiobjective Continuous Ant Colony Optimized Fuzzy Controller

Juang, Chia‐Feng; Jhan, Yue-Hua; Chen, Yanming; Hsu, Chi-Ming

doi:10.1109/tcds.2017.2681181

Cited by 22 publications

(13 citation statements)

References 30 publications

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“…It is done by recording the rotation of each wheel caused by specific values through the rotary encoder. Then, by referring to the angular velocity of all the independently driven wheel, the right wheel and left wheel , the complete Equation for a differential steering [6,33,34] system can be sequentially summarized as follows…”

Section: Figure 2 Wheeled Mobile Robotmentioning

confidence: 99%

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An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

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The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

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Section: Figure 2 Wheeled Mobile Robotmentioning

confidence: 99%

“…The main task of WFR is to maintain the distance into the setpoint position safely, commonly-termed as the expected track, when it moves [1,2,3,4,5,6]. It is expected to have the ability to make decisions in the form of movements that match the contours of the ever-changing walls.…”

Section: Introductionmentioning

confidence: 99%

An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

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“…Many studies have been conducted to develop the ability of the wall following in robots using T1-FLSs tuned by methods such as genetic algorithms [ 33 ], ant colony optimization [ 34 ], bee colony optimization [ 35 , 36 ], and differential evolution [ 37 ]. In addition to that, the work [ 38 , 39 ] proposed IT2-FLSs tuned by reinforcement ant optimization and differential evolution, respectively, to establish wall-following behavior.…”

Section: Introductionmentioning

confidence: 99%

Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems

Muthugala

Samarakoon

Rayguru

et al. 2020

Sensors

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Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

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“…A lógica fuzzyé uma das mais bem sucedidas técnicas aplicadas ao projeto controladores. A técnica surgiu a partir de estudos realizados por Zadeh (1965) e até os dias atuaisé utilizada nas mais diversas aplicações noâmbito do controle de processos (Pasieka et al, 2017;Juang et al, 2017;Mendes et al, 2019). Uma dessas aplicações consiste na geração dinâmica de set points, para controladores PID's presentes na instrumentação original de plantas industriais.…”

Section: Introductionunclassified

Sistema de Controle Fuzzy Aplicado ao Ajuste de Pentano no Gás Liquefeito de Petróleo Utilizando um Sensor Virtual Baseado em Rede Neural Artificial

Sousa

Araújo

Lima

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Control techniques based on fuzzy logic have been applied as dynamic setpoint generators for controllers present in the regulatory control layer of industrial plants.This technique is called advanced control and has become widely used. This work describes the development of a fuzzy controller to perform as a dynamic setpoint generator to control the ipentane molar fraction present in the Liquefied Petroleum Gas. As a hard-to-measure variable, the amount of i-pentane will be estimated by an artificial neural network based sensor. Results have shown that the proposed system is capable of controlling the molar fraction of i-pentane. Based on a real plant located in the city of Guamare-Brazil, A simulated plant was used for testing and validation purposes. Resumo: Técnicas de controle baseadas em lógica fuzzy vêm sendo aplicadas como geradores dinâmicos de set points para controladores presentes na camada de controle regulatório de plantas industriais. Esta técnica recebe o nome de controle avançado e tem se tornado cada vez mais utilizada. Este trabalho descreve o desenvolvimento de um controlador fuzzy para atuar como gerador dinâmico de set points para controlar a fração molar i-pentano presente no composição do Gás Liquefeito de Petróleo. Por ser uma variável de difícil medição, a quantidade de i-pentano será estimada a partir de um sensor baseado em redes neurais artificiais. Os resultados demonstram que o sistema final conseguiu ajustar a fração molar de i-pentano para todos os valores desejados. Utilizou-se uma simulação computacional, para fins de testes e validação, baseada em uma planta real que está localizada no município de Guamaré-RN.

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Evolutionary Wall-Following Hexapod Robot Using Advanced Multiobjective Continuous Ant Colony Optimized Fuzzy Controller

Cited by 22 publications

References 30 publications

An FLC-Pso Algorithm-Controlled Mobile Robot

An FLC-Pso Algorithm-Controlled Mobile Robot

Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems

Sistema de Controle Fuzzy Aplicado ao Ajuste de Pentano no Gás Liquefeito de Petróleo Utilizando um Sensor Virtual Baseado em Rede Neural Artificial

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