Robots have an underlying model to control their joints with the aim of reaching a specific pose. The accuracy of a robot is based on the underlying model and its parameters. The parameters of the underlying model of a robot are based on the ideal geometry and set by the manufacturer. If the parameters do not represent the physical robot accurately, the movements of the robot become inaccurate.

Methods to optimize the parameters to represent the physical robot more accurately exist and result in an accuracy enhancement. Nevertheless, the underlying model of the manufacturer remains of analytical form and therefore has a limited complexity which hinders the model to represent arbitrary non-linearities and higher degree relations. To further enhance the accuracy of a robot by using a model with a higher complexity, this work investigates the use of a model of the inverse kinematics based on Artificial Intelligence (AI).

The accuracy is investigated for a robot with an attached tool. In a first step, the development and initial evaluation of a suitable AI model is conducted in a simulated environment. Afterwards, the uncompensated accuracy of the robot with the tool is assessed and measurements are recorded. Using the measurements, an AI model based on the measurements of physical robot. The model is evaluated on the physical robot with a tool to quantify the achieved accuracy enhancement.