Enhancing the Performance of the Wall-Following Robot Based on FLC-Ga

Suwoyo, Heru; Tian, Yingzhong; Hajar, Muhammad Hafizd Ibnu

doi:10.22441/sinergi.2020.2.008

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…The kinematic configuration shown by Figure 1 is adopted from the wheeled mobile robot consist of two layers of the body, and the main controller named as Arduino Mega [29,30,31,32]. It has three wheels, which is an unpowered wheel and two powered wheels completed with the rotary encoder, three distance sensors placed on a specific side, as clearly mentioned before.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

Self Cite

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“…Several ways have been proposed to enhance wall-following behavior, including machine learning algorithms (Hammad et al, 2019, Teng et al, 2020 and the fuzzy logic controller (FLC) (Omrane et al, 2016, Fatmi et al, 2006, Malhotra and Sarkar 2005, Faisal et al, 2013. The present study employs the fuzzy logic controller (FLC) for the following reasons: 1) FLC has been proven as an effective tool and is widely used for improving wall-following behavior because of its outstanding ability to deal with complex uncertainty, such as various decision-making rules (Suwoyo et al, 2020). 2) FLC is more applicable because it can be simply coded and computed.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

Development of a Fuzzy Logic Controller for Autonomous Navigation of Building Inspection Robots in Unknown Environments

Chang

Siu

2023

J. Comput. Civ. Eng.

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Robotic building inspection is gaining popularity as a way to increase the security, productivity, and cost-effectiveness of traditional inspection tasks. Despite the development of numerous building inspection robotic platforms, their motions still require manual control. To facilitate full automation, there is a need to explore autonomous navigation strategies for building inspection robots. Although various autonomous navigation strategies have been developed in the robotics field, few of them are suitable for building structural inspection behavior. In accordance with the responsibilities of professional inspectors, the robot is required to follow the structural components within a desired distance and dynamically avoid obstacles to conduct in-depth scanning. This navigation task becomes more difficult when providing smooth following path in special building scenarios, such as narrow corners. Motivated by this need, the present study aimed to explore autonomous navigation for building inspection robots. To save the cost of map construction, the local navigation strategies, which control the robots' travel in unknown environments, were targeted.Specifically, the objective is to develop a robust fuzzy logic controller (FLC) for wall-following behavior. The inputs are the distances within the designed interval ranges, which were measured with a 360-degree laser. The membership functions and the decision-making rules were designed based on robot and camera configurations, building designs, and structural inspection criteria. The outputs are the real-time angular and linear velocities. Tested in both simulation and real-world environments, the novelty of the designed FLC is: 1) enabling "finding wall," "wall-following," "turning," and "obstacle avoidance" behaviors in various unknown building scenarios; 2) preventing wavy motions; and 3) preventing path deviations for arbitrary surfaces. The results can be employed to perform daily structural inspections, and they are dedicated to automating the building inspection tasks. However, the FLC is sensitive to the reflective components because of the limitations of the position sensors.

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“…In the fuzzy PID control algorithm, the input of the fuzzy controller is the difference between the target speed and the actual speed and the rate of change of the speed difference, the output is the three parameters 𝑘𝑝1, 𝑘𝑖1, and 𝑘𝑎1 of the PID controller, and the input of the PID controller is the target speed and the actual speed. The difference in speed, the output is the desired acceleration of the vehicle (Rout et al, 2016;Suwoyo et al, 2020).…”

Section: Algorithm Design Of Cruise Controlmentioning

confidence: 99%

Dubin’s Curve of RRT* Merged With A*

Abdurohman

Kang

Hidayat

2022

IJEC

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ACC (Adaptive Cruise Control) is a crucial technology to control the automatic driving of a vehicle, which can automatically adjust the driving status of the vehicle according to the driving status of the vehicle in front so that the vehicle always keeps a safe distance from the vehicle in front, which can primarily relieve the driver's driving pressure and avoid tailgating. The research is of great significance to the realization of intelligent driving of vehicles. In this paper, the fuzzy PID control method is used to study the vehicle ACC system. Firstly, the basic concept and key components of the ACC system are explained, the vehicle longitudinal dynamics model is established, and the primary ranges of , and parameters are determined through simulation; based on this, the fuzzy PID control algorithm of vehicle ACC is designed considering three different driving conditions of fixed speed and following cruise mode. The fuzzy PID control algorithm of the vehicle ACC system is designed and established. The joint simulation model of MATLAB is used to simulate the vehicle ACC system under three different driving conditions. The simulation results show that the vehicle ACC system designed in this paper ensures the safety and stability of following the vehicle while providing the vehicle's ride's comfort.

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Enhancing the Performance of the Wall-Following Robot Based on FLC-Ga

Cited by 9 publications

References 28 publications

An FLC-Pso Algorithm-Controlled Mobile Robot

An FLC-Pso Algorithm-Controlled Mobile Robot

Development of a Fuzzy Logic Controller for Autonomous Navigation of Building Inspection Robots in Unknown Environments

Dubin’s Curve of RRT* Merged With A*

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