Analysis of artificial intelligence application using back propagation neural network and fuzzy logic controller on wall-following autonomous mobile robot

Budianto, Agus; Pangabidin, Rangga; Syai’in, Mat; Adhitya, Ryan Yudha; Subiyanto, Lilik; Khumaidi, Agus; Rachman, Isa; Widiawan, Beni; Joni, Koko; Nurcahya, Eka Dwi; Pratomo, Istas; Soeprijanto, Adi; Soelistijono, Rachmad Tri

doi:10.1109/isesd.2017.8253306

Cited by 22 publications

(19 citation statements)

References 10 publications

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“…In order to improve the performance of WFR, the closed-loop controller, FLC is used. Essentially, FLC processes the input membership function and makes decision with respect to the proper output based on the rulebased system [3,4,15,16,17,19,26,27]. There have been many products applying the principle of FLC, such as in the case of adjusting the air conditioner based on the actual temperature, pressing the pedal of an automatic car based on the crowds around, and adjusting the heat based on the food maturity placed on the barbeque cooker.…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…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 main interest of the work was to improve the performance in terms of time taken for following a wall, and the performance of maintaining a desired lateral distance was not evaluated in this work. 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.…”

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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“…The goalkeeper robot is one of the robots in Indonesian Wheeled Robot Soccer Contest which has the task of keeping the goal area from the opponent's kick ball coming from all directions. The robot which is utilized autonomous robot which moves based on numerical analysis (Budianto, et al, 2017) (Kurniawan, et al, 2017). The movements are determined by the position of the ball in the field detected by the camera.…”

Section: Introductionmentioning

confidence: 99%

Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique

Setiawan

Khumaidi

Priyonggo

et al. 2019

ATCSJ

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In this research Trigonometry Technique was implemented to predict the ball movement direction for Wheeled Soccer Robot Goalkeeper. The performance of goalkeeper robot in Wheeled Soccer Robot Contest is very important. The crucial problem with goalkeeper robot is the delay in ball detection by the camera because the results of the camera images captured are always slower than the pictures that have been captured. This causes the robot's response to blocking the opponent's kick ball being late. Trigonometry Technique is one technique that can be used to predict the direction of the ball movement based on trigonometry mathematical formulas. The input data used is the location of the last ball position (x–last ball and y-last ball) and the location of the current ball position (x-current ball and y-current ball). The outputs are the prediction of the next ball location (x-predict ball and y-predict ball) and the prediction of ball movement direction prediction. The results are the goalkeeper's robot successfully predicts the opponent's kick direction with 90% accuracy and can predict the location of the next ball very well. By implementing this method, it is expected to optimize the performance of the goalkeeper robot in saving the goal.

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Analysis of artificial intelligence application using back propagation neural network and fuzzy logic controller on wall-following autonomous mobile robot

Cited by 22 publications

References 10 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

Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique

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