Small-scale Robot Arm Design with Pick and Place Mission Based on Inverse Kinematics

Tahtawi, Adnan Rafi Al; Agni, Muhammad; Hendrawati, Trisiani Dewi

doi:10.18196/26124

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…To get a certain end-effector position angle, this method uses the cosine rule of the angle in a triangle (Pythagoras' law and trigonometric rules). This model is adapted from [14] who has made a small-scale 3 DoF arm robot using the inverse kinematic approach for pick and place missions. Therefore, each link on the robotic arm is made into a triangular shape like in Figure 2 and the solution for the inverse kinematic model is based on the following equations.…”

Section: Linksmentioning

confidence: 99%

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: Linksmentioning

confidence: 99%

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

Widyacandra

Tahtawi

Martin

2022

JITEL

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“…Nevertheless, an umbilical cable is required to control the drone from the ground so that it does not travel out of its range. • Robotic Arm: In this work, a 2-DOF robotic arm [45] has been employed to get the necessary data from the microcontroller and grab various desired objects according to the needs. The used robotic arm can drift at an angle of 0 to 180 degrees.…”

Section: B Hardware Partmentioning

confidence: 99%

Semi Wireless Underwater Rescue Drone with Robotic Arm

Meem

Osman²,

Bashar

et al. 2022

Journal of Robotics and Control

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Wireless communication with underwater drones plays a vital role in rescuing an after-ship accident, and it is considered one of the most challenging technologies nowadays. The wireless underwater drone also contributes significantly to navy, gas and oil companies, underwater infrastructure, aquaculture industry, commercial diving, and deep archeological investigations. Bangladesh, a developing country with many rivers and seashores used by many water transport vehicles, faces boat and ship accidents as a typical scenario every year. Many lives, dead bodies, ships, boats, and valuable objects do not get rescued because of the lifetime risk of the divers or the rescue team. The rescue operation is exceptionally troublesome because of the absence of wireless underwater drone technology at the cheapest cost, whose innovation and function are very challenging for Bangladesh. Considering these problematic situations, in this paper, we have proposed a prototype of a semi wireless underwater drone designed and structured with the best quality PVC pipe of 6mm diameter, considering the weight as lowest as possible, maintaining its buoyancy properly. In this prototype, we have used three propellers, each connected with three servo motors, to move it up, down, back, and forth. A robotic arm is also utilized to rescue objects from under the water, and a 4K HD EKEN camera and two waterproof fog lights to search and visualize underwater objects on the mobile screen from the land. For wireless communication between the controller and the drone, we have used two nRF24L01 modules; one is for the controller, and the other is for the drone’s receiver to send and obtain the signals from the controller to the drone. 12V and 24V lithium-ion batteries are employed as power sources for the controller and receiver, respectively. Finally, real experimental tests of the proposed underwater device were performed in a swimming pool facility.

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“…Inverse kinematics calculations were performed using the geometric approach. Using the analysis results, it was observed that the real-time control of the robot arm was performed via Arduino and the error rate was obtained 3% [48]. When the literature studies are examined, it is seen that the kinematic analysis of the robot arm with many different degrees of freedom has been emphasized with various approaches and the studies in this field are currently continuing.…”

Section: Introductionmentioning

confidence: 99%

RRR Robotik Kol için Çok Katmanlı Algılayıcı ve Ters Kinematik Karşılaştırması

et al. 2024

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In this study, the position control simulation of a 3 Degree of Freedom (3DOF) robot arm was compared with machine learning and inverse kinematic analysis separately. The considered robot arm is designed in RRR pattern. In the inverse kinematic analysis of the robot arm, the geometric approach and the analytical approach are used together. Multi-Layer Perceptron (MLP) was used as a machine learning method. Some of the coordinate data that the robot arm can reach in the working space are selected and the MLP model is trained with these data. When training was done with MLP machine learning method, the correlation coefficient (R2) was obtained as 1. Coordinates of 3 different geometric models (helix, star and daisy) that can be included in the working space are used as test data of the MLP model. These tests are simulated in 3D in MATLAB environment. The simulation results were compared with the inverse kinematics analysis data. As a result, Mean Relative Error (MRE) values for helix, star and daisy shapes were calculated as 0.0007, 0.0033 and 0.0011, respectively, in the tests performed. Mean Squared Error (MSE) values were obtained as 0.0034, 0.0065 and 0.0040, respectively. This confirms that the proposed MLP model can operate this system at the desired stability.

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Small-scale Robot Arm Design with Pick and Place Mission Based on Inverse Kinematics

Cited by 6 publications

References 20 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

Semi Wireless Underwater Rescue Drone with Robotic Arm

RRR Robotik Kol için Çok Katmanlı Algılayıcı ve Ters Kinematik Karşılaştırması

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