Abstract
Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place a tool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neural network, more specifically, a feed-forward type, multilayer perceptron (MLP), is trained, so that it could be used to calculate the inverse kinematics for a robotic manipulator. First, direct kinematics of a robotic manipulator are determined using Denavit–Hartenberg method and a dataset of 15,000 points is generated using the calculated homogenous transformation matrices. Following that, multiple MLPs are trained with 10,240 different hyperparameter combinations to find the best. Each trained MLP is evaluated using the R 2 and mean absolute error metrics and the architectures of the MLPs that achieved the best results are presented. Results show a successful regression for the first five joints (percentage error being less than 0.1%) but a comparatively poor regression for the final joint due to the configuration of the robotic manipulator.
Highlights
Determining kinematic properties of a robotic manipulator is a crucial step in any work relating to the use of the robotic manipulator
Inverse kinematic equations allow the opposite transformation from the tool configuration space to the joint variable space, that is, if we know the position in the workspace that we are trying to achieve, we can calculate the joint values necessary to position the tool at that location
While the determination of the robotic manipulator direct kinematics is relatively straightforward, and there are methods such as Denavit–Hartenberg (D-H) that allow for the simple determination, determining inverse kinematics is a more
Summary
Determining kinematic properties of a robotic manipulator is a crucial step in any work relating to the use of the robotic manipulator. Before any further calculations can be performed, direct and inverse kinematic equations need to be determined. Direct kinematic equations allow the transformation from the joint variable space into the tool configuration space, that is, calculation of the position of tool in workspace from predefined joint rotation values.. Inverse kinematic equations allow the opposite transformation from the tool configuration space to the joint variable space, that is, if we know the position in the workspace that we are trying to achieve, we can calculate the joint values necessary to position the tool at that location. Determining the inverse kinematic equations for complex robots has high algebraic complexity..
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