
<abstract><p>Accuracy is an important factor to consider when evaluating the performance of a manipulator. The accuracy of a manipulator is determined by its ability to accurately move and position objects in a precise manner. This research paper aims to evaluate the performance of different methods for the kinematic analysis of manipulators. The study employs four distinct techniques, namely mathematical modeling using the closed form solutions method, roboanalyzer, Peter Corke toolbox, and particle swarm optimization, to perform kinematic analysis for manipulators. The KUKA industrial manipulator is used as an illustrative case study in this research due to its widespread use in various industrial applications in addition to its high precision and stability. Its wide usage in the industry makes the results of this research highly relevant and allows for a thorough evaluation of the performance of the different methods being studied. Furthermore, understanding the kinematic analysis of the manipulator can also help in improving the performance and increasing the efficiency of the robot in different tasks. This paper conducts a comparison of the accuracy of the four methods, and the results indicate that particle swarm optimization is the most accurate method. The RoboAnalyzer approach achieved the fastest execution time.</p></abstract>
Artificial intelligence, Kinematics, kinematic analysis, Robot, Economics, Kinematic and Dynamic Analysis of Robot Manipulators, Ocean Engineering, Control (management), Sampling-Based Motion Planning Algorithms, Application of Diagnostic Techniques in Oil Wells, Engineering, Manipulator (device), kuka industrial manipulator, Machine learning, QA1-939, Control theory (sociology), Classical mechanics, Stability (learning theory), Kinematic Analysis, Position (finance), particle swarm optimization, Robot manipulator, Particle swarm optimization, Control engineering, Physics, roboanalyzer, Computer science, peter corke toolbox, Programming language, Algorithm, Control and Systems Engineering, Physical Sciences, Computer Science, Computer Vision and Pattern Recognition, Toolbox, Mathematics, Simulation, Finance
Artificial intelligence, Kinematics, kinematic analysis, Robot, Economics, Kinematic and Dynamic Analysis of Robot Manipulators, Ocean Engineering, Control (management), Sampling-Based Motion Planning Algorithms, Application of Diagnostic Techniques in Oil Wells, Engineering, Manipulator (device), kuka industrial manipulator, Machine learning, QA1-939, Control theory (sociology), Classical mechanics, Stability (learning theory), Kinematic Analysis, Position (finance), particle swarm optimization, Robot manipulator, Particle swarm optimization, Control engineering, Physics, roboanalyzer, Computer science, peter corke toolbox, Programming language, Algorithm, Control and Systems Engineering, Physical Sciences, Computer Science, Computer Vision and Pattern Recognition, Toolbox, Mathematics, Simulation, Finance
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
