Smart Robot Wall-Following Control Using a Sonar Behavior-based Fuzzy Controller in Unknown Environments

Lee, Chin-Ling; Lin, Cheng‐Jian; Lin, Hsueh-Yi

doi:10.1080/23080477.2017.1348061

Cited by 10 publications

(13 citation statements)

References 9 publications

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“…To address mobile robot control in unknown environment, Lee et al [281] proposed sonar behavior-based fuzzy controller (BFC) to control mobile robot wall-following. The sub-fuzzy controllers with nine fuzzy rules were used for the control.…”

Section: Other Hybrid Methods That Include Flmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

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Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

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Section: Other Hybrid Methods That Include Flmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Much work has been conducted on the development of T1-FLSs based on expert knowledge for developing wall-following ability in mobile robots [ 40 , 41 , 42 , 43 ]. Moreover, these FLSs based on expert knowledge have been proposed to resolve the limitation of FLSs tuned through optimization methods.…”

Section: Introductionmentioning

confidence: 99%

“…The T1-FLS proposed in [ 41 ] utilizes sonar sensor readings of a mobile robot to realize wall-following behavior. The work [ 42 , 43 ] proposed a behavior based fuzzy controller consisting of three independent T1-FLSs for wall-following. This controller switches between the three T1-FLSs based on the contexts of wall following, straight, left corner, and right corner.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the switching might be rigid in the case of sensor noises. Furthermore, three independent T1-FLSs were required for realizing the wall following using the controller proposed in [ 42 , 43 ]. The T1-FLSs proposed in [ 40 , 41 , 42 , 43 ] were capable of realizing the wall-following behavior of a mobile robot, and they could resolve the limitation of FLSs tuned through optimization methods.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, three independent T1-FLSs were required for realizing the wall following using the controller proposed in [ 42 , 43 ]. The T1-FLSs proposed in [ 40 , 41 , 42 , 43 ] were capable of realizing the wall-following behavior of a mobile robot, and they could resolve the limitation of FLSs tuned through optimization methods. Nevertheless, the FLSs proposed in [ 40 , 41 , 42 , 43 ] were designed considering a specific lateral distance maintained from a wall.…”

Section: Introductionmentioning

confidence: 99%

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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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WITHDRAWN: Design and Implementation of Human-Computer Interaction Adjustment in Nuclear Power Monitoring System

Liu

Zhang

2021

Microprocessors and Microsystems

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Smart Robot Wall-Following Control Using a Sonar Behavior-based Fuzzy Controller in Unknown Environments

Cited by 10 publications

References 9 publications

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

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

WITHDRAWN: Design and Implementation of Human-Computer Interaction Adjustment in Nuclear Power Monitoring System

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