Colored Object Sorting using 5 DoF Robot Arm based Artificial Neural Network (ANN) Method

Sanjaya, W. S. Mada; Anggraeni, Dyah; Munawwaroh, Madinatul; Nurasyidiek, M. Yusuf Solih; Rahayu, Darmawan Setia; Samsudin, Achmad; Santika, Ikhsan Purnama; Palupi, Endah Kinarya

doi:10.1088/1742-6596/1090/1/012070

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Table 2 shows that the data result of angle with ANFIS has a very close resemblance, whereas in Table 3 it can be seen that the angle of the training data does not have a close resemblance to the inverse kinematics yield angle. The difference in the results of inverse kinematics with training data is influenced by the servo geometry of the camera which is not parallel so it is difficult to make mathematical models that can accurately describe the geometry [14]. From the arm manipulatormaking process, it can be concluded that one DOF which has varied angular motion can be ascertained its movement towards the coordinates of the object described by two methods: ANFIS method and inverse kinematics method.…”

Section: Resultsmentioning

confidence: 99%

Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system

Palupi

Umam²,

Junaidi

et al. 2021

IJECS

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Robotics technology is known as a great technology demand to be developed continuesly. One of the important things that need to be considered is the control of the motion of the robot. Movement predictions can be modeled in mathematical equations. Prediction based on learning logic is also very supportive of motion control systems, especially arm motion. In this study, the authors combined the two methods as the main study. The working principle of the arm is to take colored objects detected by the camera. In this study, we made arm four DOFs (Degree of Freedom), but only one DOF is controlled by ANFIS because the other three DOFs only move at two fixed angles. Two methods of determining the arm angle of motion used are inverse kinematics and ANFIS methods. The angle of motion and the position of the red object can be observed in real-time on the monitor with the interface in the MATLAB GUI. The angular output that appears in the MATLAB GUI is sent to Arduino in the form of characters, then, Arduino translates it into servo motion to the coordinates of the object detected by the camera. The results showed that the ANFIS method was more effective than the inverse kinematics model.

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Section: Resultsmentioning

confidence: 99%

Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system

Palupi

Umam²,

Junaidi

et al. 2021

IJECS

View full text Add to dashboard Cite

show abstract

“…Furthermore, the potential of a robot arm must be recognized. As an example, one study uses a robotic arm and an Artificial Neural Network (ANN) to classify objects based on their colour [16]. An image processing system detects the object and generates an inverse kinematic model, which is then trained with an ANN system.…”

Section: Mamdani Fuzzy Interference System (Fis)mentioning

confidence: 99%

Robotic Arm Position Control using Mamdani Fuzzy Logic on Arduino Microcontroller

Hamzah¹,

Thamrin²,

Tajjudin³

2022

JMechE

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A robotic arm is the most often used robot in manufacturing to perform the same task accurately again and over again in a controlled environment. However, direct positioning control of the robotic arm is always inaccurate as it does not consider its position in the external environment. Therefore, this work implements the Mamdani fuzzy logic in the position control of the 6 DOF robotic arm to improve the accuracy and movement of each joint at this manipulator. The implemented fuzzy logic Mamdani inference system is done in MATLAB and finally converted to the C language to accommodate the main microcontroller, Arduino UNO environment. There are 6 servo motors controlled by this approach and the result is compared with the conventional method. It is found that the Mamdani fuzzy logic controller has an average error of 0.67% while the direct control method has an average error of 2.33%. Based on this result, it shows that the use of the Mamdani fuzzy logic increases the accuracy and reduces the swaying movement after stopping the robotic arm movement.

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A neural network model used in continuous grain dryer control system

et al. 2021

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Colored Object Sorting using 5 DoF Robot Arm based Artificial Neural Network (ANN) Method

Cited by 7 publications

References 10 publications

Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system

Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system

Robotic Arm Position Control using Mamdani Fuzzy Logic on Arduino Microcontroller

A neural network model used in continuous grain dryer control system

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