scholarly journals Design and Control of an Automated SCARA Robotic Arm with a Pneumatic Soft Gripper

2021 ◽  
Vol 2070 (1) ◽  
pp. 012207
Author(s):  
V K Pranav ◽  
Nitheesh Kumar G ◽  
V Yadukrishnan ◽  
Krishnanand Anil ◽  
S Ghanashyam ◽  
...  
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Acrylonitrile Butadiene Styrene ◽  
Nitrile Rubber ◽  
Robotic Arm ◽  
End Effector ◽  
Pick And Place ◽  
Predicted Values ◽  
And Control ◽  
Butadiene Styrene

Abstract The paper focuses on the design, analysis and control of an automated 3 DOF SCARA robotic manipulator with an end effector made of asymmetric flexible pneumatic bellow actuator (AFPBA). The manipulator is made for use in the poultry industry and therefore tested in its ability to detect, pick and place eggs. The links of the manipulator are made using acrylonitrile butadiene styrene (ABS) and the end effector is made using nitrile rubber. A neural network derived from the VGG-16 and YOLO architecture is then implemented to detect and localize eggs. The predicted values were then used to calculate the inverse kinematics of the manipulator.

Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

2019 ◽  
Vol 16 (04) ◽  
pp. 1950012 ◽  
Author(s):  
Mircea Hulea ◽  
Adrian Burlacu ◽  
Constantin-Florin Caruntu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Control Method ◽  
Inhibitory Neurons ◽  
Robotic Arm ◽  
Control Approach ◽  
Planning And Control ◽  
Robotic Joints ◽  
And Control

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

The diffusivity modeling of different gases in acrylonitrile butadiene styrene (ABS)

2020 ◽  
pp. 089270572097619
Author(s):  
Hamidreza Azimi
Keyword(s):  
Chemical Potential ◽  
Acrylonitrile Butadiene Styrene ◽  
Magnetic Suspension ◽  
Magnetic Suspension Balance ◽  
Proposed Model ◽  
Normal Hexane ◽  
Temperature And Pressure ◽  
Predicted Values ◽  
Different Gases ◽  
Butadiene Styrene

In this work, we used three gases (CO2, N2 and normal hexane) for diffusivity measurements in Acrylonitrile butadiene styrene (ABS). We proposed a diffusion model that the diffusion coefficients of each gas in ABS could be estimated from the specific volume of ABS/gas mixture and chemical potential of gas in ABS. The solubility and diffusivity of three gases into ABS were determined by a magnetic suspension balance. The results showed that the solubility and diffusivity of three gases increased with increasing of pressure. Also it was determined that N2 has a lowest solubility and the highest diffusivity in ABS in all temperature and pressure ranges. It was shown that there was a suitable overlapping between the experimental and predicted values from the proposed model, in which the proposed model could successfully estimate the diffusion coefficient of mentioned gases in ABS in all temperature and pressure ranges.

Inverse Kinematics Modelling and Simulation for Upper Case Writing Robot Control Using ANFIS

2016 ◽  
Vol 836 ◽  
pp. 37-41 ◽  
Author(s):  
Adlina Taufik Syamlan ◽  
Bambang Pramujati ◽  
Hendro Nurhadi
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Inverse Kinematics ◽  
Robot Control ◽  
Industrial Revolution ◽  
Training Data ◽  
Robotic Arm ◽  
End Effector ◽  
Precision And Accuracy ◽  
Case Writing

Robotics has lots of use in the industrial world and has lots of development since the industrial revolution, due to its qualities of high precision and accuracy. This paper is designed to display the qualities in a form of a writing robot. The aim of this study is to construct the system based on data gathered and to develop the control system based on the model. There are four aspects studied for this project, namely image processing, character recognition, image properties extraction and inverse kinematics. This paper served as discussion in modelling the robotic arm used for writing robot and generating theta for end effector position. Training data are generated through meshgrid, which is the fed through anfis.

scholarly journals Design Of 4DOF 3D Robotic Arm to Separate the Objects Using a Camera

2021 ◽  
Vol 3 (1) ◽  
pp. 27-35
Author(s):  
Akhmad Fahruzi ◽  
Bimo Satyo Agomo ◽  
Yulianto Agung Prabowo
Keyword(s):  
Inverse Kinematics ◽  
Robotic Arm ◽  
Robot Arm ◽  
Fixed Position ◽  
End Effector ◽  
Robotic Arms ◽  
Work Area ◽  
Intended Object ◽  
Place Of Origin ◽  
Prototype Design

Nowadays robotic arm is widely used in various industries, especially those engaged in manufacturing. Robotic arms are usually used to perform jobs such as picking up and moving goods from their place of origin to the location desired by the operator. In this study, a 3d 4 DOF (Degree of Freedom) robotic arm. The prototype was made to move goods with random coordinates to places or boxes whose coordinates were determined in advance. The robot can know the coordinates of the object to be taken or moved. The arm robot prototype design is completed with a camera connected to a computer, where the camera is installed statically (fixed position) above the robot's work area. The camera functions like image processing to detect the object's position by taking the coordinates of the object. Then the object coordinates will be input into inverse kinematics that will produce an angle in every point of the servo arm so that the position of the end effector on the robot arm can be founded and reach the intended object. From the results of testing and analysis, it was found that the error in the webcam test to detect object coordinates was 2.58%, the error in the servo motion test was 12.68%, and the error in the inverse kinematics test was 7.85% on the x-axis, the error was 6.31% on the y-axis and an error of 12.77% on the z-axis. The reliability of the whole system is 66.66%.

Integrated Design of Active And Passive Gripper For Robotic Pick And Place Applications

2021 ◽  
Author(s):  
Mahmud Huseynov
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Maximum Stress ◽  
Integrated Design ◽  
Acrylonitrile Butadiene Styrene ◽  
Element Analysis ◽  
Pick And Place ◽  
Final Design ◽  
Stability Issues ◽  
Butadiene Styrene

Abstract Grippers developed in recent years either cannot achieve a complete universality due to grasping stability issues or are designed with excessive complexity. In this paper, the design of a three-fingered tendon-driven integrated gripper based on the concept of integrating Universal Active Gripper (UAG) with Universal Passive Gripper (UPG) fingertips, which ensures achieving adjustable fingertip stiffness and is practically proven to solve grasping stability issues, is proposed. Furthermore, kinematic, dynamic, and force analyses was conducted to calculate the specifications of the designed gripper, which was compared to four commercial grippers. Finite element analysis was also carried out for the designed gripper. When the final design of the gripper was compared to that of Hou et al.'s (2018) (the only comparable design present for the integrated gripper), one crucial similarity noted was based on both designs adopting the same kinematic configuration. This fact further increases the confidence in the optimal development of the designed gripper. According to FEA results, the maximum stress acting on the components of the gripper was 15.78 MPa, 39.45% of the yield stress of Acrylonitrile Butadiene Styrene (ABS). In conclusion, it was theoretically established that the designed gripper with the tendon-driven actuation is operating efficiently.

scholarly journals Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Takehiko Ogawa ◽  
Hajime Kanada
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Inverse Kinematics ◽  
Robot Arm ◽  
Joint Angles ◽  
End Effector ◽  
Inverse Kinematics Problem ◽  
Ill Posed ◽  
The Given

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

Kinematics control of redundant manipulators using a CMAC neural network combined with a genetic algorithm

Robotica ◽  
2004 ◽  
Vol 22 (6) ◽  
pp. 611-621 ◽  
Author(s):  
Yangmin Li ◽  
Sio Hong Leong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control Systems ◽  
Inverse Kinematics ◽  
Robot Control ◽  
Control Problems ◽  
Redundant Manipulators ◽  
Cerebellar Model Articulation Controller ◽  
Cmac Neural Network ◽  
And Control

A method is proposed to solve the inverse kinematics and control problems of robot control systems using a cerebellar model articulation controller neural network combined with a genetic algorithm. Computer simulations and experiments with a 7-DOF redundant modular manipulator have demonstrated the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document