Positioning control of robots using a novel nature inspired optimization based neural network
Research Square
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Positioning control methods for robot arms are offered within a broad framework for the purposes of unification and categorization. In robot control, the issue of Inverse Kinematics (IK) is crucial. Several conventional IK solutions, including geometry, iterations, and algebraic approaches, are insufficient for high-speed solutions and precise positioning. In recent years, the subject of robot IK using neural networks has attracted a great deal of attention, although its precise control is convenient and need improvement. To tackle the IK issue of a UR3 robot, we offer the Global Iterative Sunflower Optimized Binary Multi Layer Perceptron (GISOB-MLP) neural network technique. The GISOB-MLP improves upon previous methods in terms of generalizability, convergence speed, and convergence accuracy. In order to perform medical puncture surgery, which necessitates precise robot positioning to within 1 mm, the study method solves the position error for the UR3 robot IK only with joint angle or less 0.1 degree courses and the output end tool less than 0.1 mm. The study was conducted, and initial results were obtained, with the goal of pinpointing the robot's puncturing application; this laid the groundwork for the robot's eventual widespread use in medical settings.
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