Pemodelan Forward Kinematic dan Inverse Kinematic Robot Berlengan PUMA 560

Surriani, Atikah; Arrofiq, Muhammad; Fahmizal, Fahmizal

doi:10.26555/jiteki.v4i2.11795

Cited by 2 publications

(3 citation statements)

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“…The position values of the coordinates X, Y, and Z are entered based on Table 2. Then these values are put into equation (3) to equation (12) angle/theta (θ 1 , θ 2 , and θ 3 ) the obtained value is added to the offset value due to the different rotation conditions of the 3-DoF AX-12A robot arm, which is 300°, so the actual theta value is θ 1 = θ 1 -30°, θ 2 = θ 2 + 60°, and θ 3 = θ 3 + 150°.…”

Section: Inverse Kinematic Modeling Resultsmentioning

confidence: 99%

“…After modeling with both kinematics analysis has been carried out, the results can be implemented into the program and simulated using several software such as [11] with simulation using V-REP to analyze a 3-DoF robot arm with forward kinematics that obtains the error percentage is 4.99% for the x-coordinate, 5.57% for the y-coordinate, and 3.18% for the z-coordinate. This is different from what was done in research [12] and [13] which chose kinematics modeling on the PUMA 560 robot arm and AL5A robot arm using MATLAB simulation with the RVC robotics toolbox feature from Peter I. Corke.…”

Section: Introductionmentioning

confidence: 89%

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Forward and inverse kinematics modeling of 3-DoF AX-12A robotic manipulator

Widyacandra

Tahtawi

Martin

2022

JITEL

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The presence of robots that can assist humans with heavy or dangerous work makes the need for robots more pressing at the moment. One type of robot needed is a robot arm, which is widely used in the manufacturing industry, such as in the assembly process and pick and place. The types of robotic arms used vary both in terms of configuration and the number of degrees of freedom. However, with different types of robotic arms, different models of movement are used. Therefore, research related to the modeling of the robotic arm continues to be carried out to obtain the appropriate movement of the robotic arm. One of the methods used as a first step in designing a robotic arm movement model is kinematics analysis. Kinematics analysis aims to analyze the movement of the robot arm without knowing what force causes the movement. This paper aims to produce an ideal movement model for the AX-12A 3-DoF robotic arm using forward kinematic and inverse kinematic analysis using two methods, the Denavit-Haterberg method and the geometric approach method. The difference from other papers is that this paper makes the kinematics model using Robotic, Vision, and Control (RVC) tools based on the Peter I. Corke model on MATLAB software first before implementing it on hardware. The results show that the error percentage for the forward kinematic model is 1.04% and the inverse kinematic is 0.76%, which means the two models achieved the target that the model’s error maximum must be less than 2%.

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Section: Inverse Kinematic Modeling Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 89%

Forward and inverse kinematics modeling of 3-DoF AX-12A robotic manipulator

Widyacandra

Tahtawi

Martin

2022

JITEL

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“…4 shows different robots that are compatible with the robotic vision system. These robots include the mechanical arm robot [91], [92], scala robot, and cartesian robot [93]- [97]. Additionally, industrial motors can be integrated into the robotic vision system.…”

Section: A Robotic Visionmentioning

confidence: 99%

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

Chotikunnan,

Puttasakul,

Chotikunnan

et al. 2023

Journal of Robotics and Control

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This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications.

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Pemodelan Forward Kinematic dan Inverse Kinematic Robot Berlengan PUMA 560

Cited by 2 publications

References 0 publications

Forward and inverse kinematics modeling of 3-DoF AX-12A robotic manipulator

Forward and inverse kinematics modeling of 3-DoF AX-12A robotic manipulator

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

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