Design and implementation of a fuzzy logic-based joint controller on a 6-DOF robot arm with machine vision feedback

Ligutan, Dino Dominic; Cruz, Levin Jaeron S.; Rosario, Michael Carlo D. P. Del; Kudhal, Jho Nathan Singh; Abad, Alexander C.; Dadios, Elmer P.

doi:10.1109/sai.2017.8252111

Cited by 16 publications

(14 citation statements)

References 16 publications

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“…8: The Mamdani Rule applied on Fuzzy Inference System (FIS) in form of IF-THEN statement, this was an easy rule and have been used on computation a lot. The defuzzification process transformed the fuzzy output to crip output, using the Centre of Gravity Method (COG) [12]. The modification of angular position from the secondary parabola and the temperature difference from the sensor processed with 25 control rules of Fuzzy Mamdani.…”

Section: Fuzzy Controllermentioning

confidence: 99%

“…Some control system research and movement optimization for the multi-degree freedom robotic arm with various methods e.g., [12][13][14] generally was utilized for finding one coordinate point (x, y, z) axis. The optimization searching process of a certain angle on the 3-DOF robotic arm for DPDC was established with a Genetic Algorithm (GA) with the flux distribution output evenly on the receiver absorber [11].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control of Robotic Arm System to Improve Flux Distribution on Dual Parabola Dish Concentrator

Wardhana¹,

Ashari²,

Suryoatmojo³

2020

IJIES

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To concentrate the rays to the focal point and stabilize the temperature on the receiver were one of the complex works of the two-stage solar concentrator. This paper offers a new method of Fuzzy cascade controller system based on tuning up the optimization at Genetic Algorithm-simple additive weighting (GA-SAW) on the dual parabolic dish concentrator with Compound Parabolic Concentrator. The model and computation of three degrees of freedom robotic arm movement with the ray tracing method were used as the concentrator position predictor. In this research, the acquired fuzzy controller result had an average settling time 0.497 sec, and average rise time 0.277 sec faster than a conventional PID controller. This research was able to overcome the disturbance which was diffused rays, so there was a better output with the power and heat flux increased up to 62.49%. The monte carlo ray tracing method from Tonatiuh Software was used to investigate and movement validation of the dual parabola concentrator by showed the flux distribution on the receiver's absorber. In the last stage, an experiment with a prototype had been conducted as a response verification of the controller system which produce a receiver temperature output as high as 121ºC. The final result showed that the controller system managed to optimize the temperature on the receiver's absorber and generate a stable thermoelectric output with power of 1.01 Watt.

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Section: Fuzzy Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Control of Robotic Arm System to Improve Flux Distribution on Dual Parabola Dish Concentrator

Wardhana¹,

Ashari²,

Suryoatmojo³

2020

IJIES

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“…Non-classical or intelligent controllers had been developed throughout the years, such as fuzzy logic based controllers [5], [6] that mimics the way humans think, artificial neural network based controllers [7], [8] that emulates the biological human brain, genetic algorithm based controllers [9], [10] inspired by evolutionary processes or hybrid types [11]. One such controller developed in this study is the fuzzy logic-based joint controller (FLJC) [4] that is capable of dealing with system nonlinearities by moving the joints of the robotic arm at proper rate and interval according to the task at hand. Fuzzy logic controllers has been shown as an effective controller in a number of robot systems like the micro soccer robots [12]- [15], micro-golf robot [16], ball-beam balancing robot [17] and simulated and actual robotic arms [4], [6], [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

“…One such controller developed in this study is the fuzzy logic-based joint controller (FLJC) [4] that is capable of dealing with system nonlinearities by moving the joints of the robotic arm at proper rate and interval according to the task at hand. Fuzzy logic controllers has been shown as an effective controller in a number of robot systems like the micro soccer robots [12]- [15], micro-golf robot [16], ball-beam balancing robot [17] and simulated and actual robotic arms [4], [6], [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

“…Aside from the controller developed in [4], this study will give emphasis on the integration of the controller with a machine vision system to demonstrate the use of the fuzzy logic controlled autonomous robotic arm system into a colorbased sorter system. The machine vision system will be thoroughly discussed as well as the algorithm deployed to perform the sorting process to realize a fully functional colorbased sorter.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Logic-Controlled 6-DOF Robotic Arm Color-based Sorter with Machine Vision Feedback

Abad¹,

Ligutan²,

Dadios³

et al. 2018

ijacsa

Self Cite

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A demonstration of the application of fuzzy logicbased joint controller (FLJC) to a 6-DOF robotic arm as a colorbased sorter system is presented in this study. The robotic arm with FLJC is integrated with a machine vision system that can discriminate different colors. Additionally, the machine vision system composed of Kinect camera and computer were used to extract the coordinates of the gripper and the objects within the image of the workspace. A graphical user interface with an underlying sorting algorithm allows the user to control the sorting process. Once the system is configured, the computed joint angles by FLJC are transmitted serially to the microcontroller. The results show that the absolute error of the gripper coordinates is less than 2 cm and that the machine vision is capable of achieving at least 95% accuracy in proper color discrimination both for first and second level stacked color objects.

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IoST-Enabled Robotic Arm Control and Abnormality Prediction Using Minimal Flex Sensors and Gaussian Mixture Models

Akhund,

Shaikh,

De La Torre Díez

et al. 2024

IEEE Access

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This work presents a groundbreaking approach with a fusion of the Internet of Sensing Things (IoST) and robotics. This system utilizes four flex sensors strategically placed on the most flexible fingers across both hands to control a Six-DoF robotic arm, offering a novel interface for those with limited mobility. This system can also be used for moving toxic objects. The Raspberry Pi is the central control unit that acquires data from the flex sensor and controls the servo motors. Moreover, the device incorporates machine learning to learn the daily movements of the users and predict abnormal finger movements. Multiple data analyses and visualization are initiated to predict the normal and abnormal data. GMMs or Gaussian Mixture Models showed successful results among various abnormality detection processes. This amalgamation of flexible sensing and mathematical modeling offers precision and adaptability in control mechanisms.

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Design and implementation of a fuzzy logic-based joint controller on a 6-DOF robot arm with machine vision feedback

Cited by 16 publications

References 16 publications

Optimal Control of Robotic Arm System to Improve Flux Distribution on Dual Parabola Dish Concentrator

Optimal Control of Robotic Arm System to Improve Flux Distribution on Dual Parabola Dish Concentrator

Fuzzy Logic-Controlled 6-DOF Robotic Arm Color-based Sorter with Machine Vision Feedback

IoST-Enabled Robotic Arm Control and Abnormality Prediction Using Minimal Flex Sensors and Gaussian Mixture Models

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