Human Expertise in Mobile Robot Navigation

Faisal, Mohammed; Algabri, Mohammed; Abdelkader, Bencherif Mohamed; Dhahri, Habib; Rahhal, Mohamad Mahmoud Al

doi:10.1109/access.2017.2780082

Cited by 25 publications

(32 citation statements)

References 16 publications

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“…In the case of wall-following behavior, the exact dynamics of the robot and an environment is not certain. Nevertheless, the required control actions for wall-following behavior could be explained using linguistic expressions based on expert knowledge [ 53 ]. Furthermore, the sensory information retrieved from the range sensors of the robot is imprecise due to sensor noise.…”

Section: Fuzzy Logic System For Wall-following Behaviormentioning

confidence: 99%

“…On the contrary, fuzzy logic has proven to be effective at inferring control actions while coping with imprecise sensor information [ 54 , 55 ]. In addition to that, fuzzy logic has often been used for the navigation of robots in unknown environments [ 53 , 56 ]. Therefore, fuzzy logic was used to establish the wall-following behavior for the robot.…”

Section: Fuzzy Logic System For Wall-following Behaviormentioning

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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Section: Fuzzy Logic System For Wall-following Behaviormentioning

confidence: 99%

Section: Fuzzy Logic System For Wall-following Behaviormentioning

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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“…The performances of point-to-point navigation by learning-based fuzzy logic systems and a fuzzy logic system pruned by human expertise were compared in [ 41 ]. According to the outcomes of the comparison, the fuzzy logic system pruned based on human experience outperformed the proposed learning-based fuzzy logic system.…”

Section: Introductionmentioning

confidence: 99%

Collision Avoidance and Stability Study of a Self-Reconfigurable Drainage Robot

Parween

Muthugala

Heredia

et al. 2021

Sensors

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The inspection and maintenance of drains with varying heights necessitates a drain mapping robot with trained labour to maintain community hygiene and prevent the spread of diseases. For adapting to level changes and navigating in the narrow confined environments of drains, we developed a self-configurable hybrid robot, named Tarantula-II. The platform is a quadruped robot with hybrid locomotion and the ability to reconfigure to achieve variable height and width. It has four legs, and each leg is made of linear actuators and modular rolling wheel mechanisms with bi-directional movement. The platform has a fuzzy logic system for collision avoidance of the side wall in the drain environment. During level shifting, the platform achieves stability by using the pitch angle as the feedback from the inertial measuring unit (IMU) mounted on the platform. This feedback helps to adjust the accurate height of the platform. In this paper, we describe the detailed mechanical design and system architecture, kinematic models, control architecture, and stability of the platform. We deployed the platform both in a lab setting and in a real-time drain environment to demonstrate the wall collision avoidance, stability, and level shifting capabilities of the platform.

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“…The main reason for not being able to cope with the problem when navigating in unknown environments is due to incomplete information in the changing environment. Intelligent techniques such as fuzzy logic (FL), optimization methods, neural networks (NN) are widely used to overcome these problems [1–4]. Thus, it is aimed to give the robot the ability to grasp the dynamic environment better.…”

Section: Introduction and Overviewmentioning

confidence: 99%

Autonomous smart robot for path predicting and finding in maze based on fuzzy and neuro‐Fuzzy approaches

Batti

Jabeur

Seddik

2020

Asian Journal of Control

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The navigation of autonomous mobile robots has in recent times gained interest from many researchers in different areas such as the industrial, agricultural, and military sectors. This paper aims at carefully investigating two advanced types of approaches for guiding a non‐holonomic mobile robot to navigate in an environment area cluttered with static obstacles. Firstly, a Fuzzy logic controller (FLC) was designed, using trapezoidal shape Membership functions (MF's). Secondly, an Adaptive neuro fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy controller. To validate the feasibility and effectiveness of the proposed models, V‐REP and MATLAB software are used. A comparative evaluation is, then, done on the basis of speed. The simulations results showed that the mobile robot could navigate successfully into maze environment with both proposed approaches but ANFIS controller provided better results in comparison to fuzzy controller.

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Human Expertise in Mobile Robot Navigation

Cited by 25 publications

References 16 publications

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

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

Collision Avoidance and Stability Study of a Self-Reconfigurable Drainage Robot

Autonomous smart robot for path predicting and finding in maze based on fuzzy and neuro‐Fuzzy approaches

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