scholarly journals Forward and Inverse Kinematics Demonstration using RoboDK and C#

2021 ◽  
pp. 97-105
Author(s):  
Sudip Chakraborty ◽  
P. S. Aithal
Keyword(s):  
User Interface ◽  
Learning Curve ◽  
Inverse Kinematics ◽  
Industrial Robot ◽  
Research Work ◽  
Arm Movement ◽  
Robotic Arm ◽  
Software Environment ◽  
Industrial Grade ◽  
Forward And Inverse Kinematics

Purpose: Robot researchers need a simulator to understand better the algorithm on path planning, arm movement, and many more. They need a good simulator. RoboDK is an excellent simulator to fulfill the research work. It has calibration facilities, so it is industrial-grade software. Its forward and inverse kinematics accuracy is better than any competing software. The main advantage is all robots under one IDE. When we use an industrial robot, and we must use their software environment to operate the robot. But the RoboDK covers most of the robots and runs under one roof. And we need to learn only one IDE. The RoboDK online library is full of the standard robot. And all robot’s operation procedure is the same. So, the learning curve of new robots is easy. It is easy to simulate, and it can connect with a practical robot to execute the task. Using this software, we can quickly create digital twins for the industry. Now we think about control the robot from our application. When we use to control the robot from an external environment or remote software, we need the use the API to control the robot. Here we will see how easily we can operate the robot from our custom application. We adopted RoboDK C# API and integrated it into Visual studio using a User interface to control the robot movement. Keeping this research as a reference, the robotic arm researcher can add value to their research. Our primary purpose is to shorten the learning curve to integrate the RoboDK with their custom application. Design/Methodology/Approach: Taking the RoboDK C# API they provided, we customized it according to our purpose with minimal components. After developing a graphical user interface, we interact through API. Then, opening both RoboDK IDE and C# application, we can send the End effector position using the sliding movement. Findings/Result: After our research, we found that RoboDK is a good IDE for our research on the robotics arm. We can easily integrate the C# API they provided with our custom application for research purposes. Originality/Value: If we want to test robotic arm movement in the simulator, we need an excellent simulator like RoboDK. Integrating the RoboDK C# API is a little bit time-consuming. Using our approach, the researcher can continue their research in a minimal period. And find adequate information here to integrate easily into their project. Paper Type: Simulation-based Research.

scholarly journals An Inverse Kinematics Demonstration of a Custom Robot using C# and CoppeliaSim

2021 ◽  
pp. 78-87
Author(s):  
Sudip Chakraborty ◽  
P. S. Aithal
Keyword(s):  
Inverse Kinematics ◽  
Design Methodology ◽  
Excellent Result ◽  
User Interaction ◽  
Arm Movement ◽  
Robotic Arm ◽  
The Real ◽  
Simulation Based ◽  
Real Robot ◽  
Definite Result

Purpose: Inverse Kinematics (I.K.) is not as easy as Forward kinematics (F.K.), where we get a definite result. I.K. algorithm provides several possible solutions. From those finding the best solution is such a critical task. For standard robots which are commercially available in the market, the user is not concerned about I.K.'s complexity. They provide the control board and programming IDE to make it easy. However, when we develop a robotic arm from our D.H. parameter and driver board, complexity arises due to lots of difficulties for executing and successful completion. To make life easy, keeping CoppeliaSim background can eliminate the calculation overhead and get good results. The custom robot is running with less computation power. It may be a good approach. We are using C# for User Interaction. Following step by step, anyone can create a robust I.K. engine with little effort. The complete code is available in GitHub to test and experiment further. Design/Methodology/Approach: The data are propagated through Interprocess communication. For the user interaction, we use visual studio IDE using the most accessible language, C#. The user interaction data are sent to another application, CoppeliaSim, which calculates inverse kinematics, and effective results are displayed through robotic arm movement. Findings/Result: Implementing this procedure can get the excellent result of the robotics arm. Furthermore, by imposing the Value on the real robot, we can get effective results. It minimizes the research overhead on I.K. calculation. Originality/Value: Without knowing I.K. calculation complexity, receiving the Value, we can apply it to the real robot. Two issues we can solve here. One is the calculation, and another one is experiment overhead. Paper Type: Simulation-based Research.

Solution for Inverse Kinematics of 5 DOF Serial Link MCM Machine

2013 ◽  
Vol 198 ◽  
pp. 53-58
Author(s):  
Piotr Kozioł ◽  
Kamil Kukliński ◽  
Adam Wolniakowski ◽  
Konstanty Miatliuk
Keyword(s):  
Mathematical Model ◽  
Industrial Application ◽  
Inverse Kinematics ◽  
Software Environment ◽  
Serial Link ◽  
Welding Machine ◽  
Interactive Animation ◽  
Forward And Inverse Kinematics ◽  
The Mathematical Model

The paper presents the forward and inverse kinematics problems solution for 5 DoF automated serial link MCM cutting and welding machine. Particularly, the solution was developed for specific application to be implemented in industry. The developing of the mathematical model for control purposes, implementing algorithms for trajectory finding and mathematical model for forward and inverse kinematics solution are described in the work. The solution is presented as simulation in RobWork Studio software [, as well as a 3D interactive animation implemented in specifically developed software environment. The result of given solution are evaluated and directions of further work concerning the implementation of obtained solution in the actual industrial application are defined.

Understanding industrial robot programming by aid of a virtual reality environment

2018 ◽  
Vol 47 (2) ◽  
pp. 135-155 ◽  
Author(s):  
Evgenia Manou ◽  
George-Christopher Vosniakos ◽  
Elias Matsas
Keyword(s):  
Virtual Reality ◽  
Collision Detection ◽  
Inverse Kinematics ◽  
Industrial Robot ◽  
Robotic Arm ◽  
Industrial Equipment ◽  
Virtual Reality Environment ◽  
Pick And Place ◽  
Detection Capabilities ◽  
Structured Questionnaire

This paper reports on the construction of a virtual environment on top of a commercially available authoring platform that simulates an industrial robotic arm in pick-and-place movement scenarios. The user interface constructed follows in functionality the well-known teach pendants but exploits just a normal PC keyboard. However, both forward and inverse kinematics is served allowing the user to command movements in either the joint or the tool coordinate system. Perception pertaining to picking and placing movements and in particular the subtle docking positions was enhanced by adding so-called perceptual aids consisting of auxiliary objects with collision detection capabilities. The application was put to test by a group of practitioner-trainees who reported favourably, using a structured questionnaire, on its merits in enhancing their understanding of the robot’s capabilities, its kinematics, as well as the task performance strategy. Thus, confidence is increased regarding the ability of Virtual Reality – based platforms to contribute to successful training regarding programming and manipulation of industrial equipment.

scholarly journals Robotic-arm assisted total knee arthroplasty is associated with improved accuracy and patient reported outcomes: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Junren Zhang ◽  
Wofhatwa Solomon Ndou ◽  
Nathan Ng ◽  
Paul Gaston ◽  
Philip M. Simpson ◽  
...  
Keyword(s):  
Learning Curve ◽  
Functional Outcomes ◽  
Patient Reported Outcomes ◽  
Meta Analysis ◽  
Robotic Arm ◽  
Component Position ◽  
Positioning Accuracy ◽  
Patient Reported ◽  
Total Knee ◽  
Improved Accuracy

AbstractThis systematic review and meta-analysis were conducted to compare the accuracy of component positioning, alignment and balancing techniques employed, patient-reported outcomes, and complications of robotic-arm assisted total knee arthroplasty (RATKA) with manual TKA (mTKA) and the associated learning curve. Searches of PubMed, Medline and Google Scholar were performed in October 2020 using PRISMA guidelines. Search terms included “robotic”, “knee” and “arthroplasty”. The criteria for inclusion were published clinical research articles reporting the learning curve for RATKA and those comparing the component position accuracy, alignment and balancing techniques, functional outcomes, or complications with mTKA. There were 198 articles identified, following full text screening, 16 studies satisfied the inclusion criteria and reported the learning curve of rTKA (n=5), component positioning accuracy (n=6), alignment and balancing techniques (n=7), functional outcomes (n=7), or complications (n=5). Two studies reported the learning curve using CUSUM analysis to establish an inflexion point for proficiency which ranged from 7 to 11 cases and there was no learning curve for component positioning accuracy. The meta-analysis showed a significantly lower difference between planned component position and implanted component position, and the spread was narrower for RATKA compared with the mTKA group (Femur coronal: mean 1.31, 95% confidence interval (CI) 1.08–1.55, p<0.00001; Tibia coronal: mean 1.56, 95% CI 1.32–1.81, p<0.00001). Three studies reported using different alignment and balancing techniques between mTKA and RATKA, two studies used the same for both group and two studies did not state the methods used in their RATKA groups. RATKA resulted in better Knee Society Score compared to mTKA in the short-to-mid-term follow up (95%CI [− 1.23,  − 0.51], p=0.004). There was no difference in arthrofibrosis, superficial and deep infection, wound dehiscence, or overall complication rates. RATKA demonstrated improved accuracy of component positioning and patient-reported outcomes. The learning curve of RATKA for operating time was between 7 and 11 cases. Future well-powered studies on RATKAs should report on the knee alignment and balancing techniques utilised to enable better comparisons on which techniques maximise patient outcomes.Level of evidence III.

A Hybrid Self-Stressed Cable-Driven Mechanism

2008 ◽  
Author(s):  
Saeed Behzadipour
Keyword(s):  
Static Loading ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Tensile Force ◽  
Cable System ◽  
End Effector ◽  
Stiffness Analysis ◽  
Cable Length ◽  
Cable Drive ◽  
Forward And Inverse Kinematics

A new hybrid cable-driven manipulator is introduced. The manipulator is composed of a Cartesian mechanism to provide three translational degrees of freedom and a cable system to drive the mechanism. The end-effector is driven by three rotational motors through the cables. The cable drive system in this mechanism is self-stressed meaning that the pre-tension of the cables which keep them taut is provided internally. In other words, no redundant actuator or external force is required to maintain the tensile force in the cables. This simplifies the operation of the mechanism by reducing the number of actuators and also avoids their continuous static loading. It also eliminates the redundant work of the actuators which is usually present in cable-driven mechanisms. Forward and inverse kinematics problems are solved and shown to have explicit solutions. Static and stiffness analysis are also performed. The effects of the cable’s compliance on the stiffness of the mechanism is modeled and presented by a characteristic cable length. The characteristic cable length is calculated and analyzed in representative locations of the workspace.

A Model of the Human Spine: Forward and Inverse Kinematics

1992 ◽  
Author(s):  
Sunil Kumar Agrawal ◽  
Siyan Li ◽  
Glen Desmier
Keyword(s):  
Range Of Motion ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Kinematic Model ◽  
Joint Angles ◽  
Human Spine ◽  
Forward And Inverse Kinematics ◽  
Position And Orientation ◽  
Three Degrees Of Freedom ◽  
Adjacent Vertebra

Abstract The human spine is a sophisticated mechanism consisting of 24 vertebrae which are arranged in a series-chain between the pelvis and the skull. By careful articulation of these vertebrae, a human being achieves fine motion of the skull. The spine can be modeled as a series-chain with 24 rigid links, the vertebrae, where each vertebra has three degrees-of-freedom relative to an adjacent vertebra. From the studies in the literature, the vertebral geometry and the range of motion between adjacent vertebrae are well-known. The objectives of this paper are to present a kinematic model of the spine using the available data in the literature and an algorithm to compute the inter vertebral joint angles given the position and orientation of the skull. This algorithm is based on the observation that the backbone can be described analytically by a space curve which is used to find the joint solutions..

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