Design of computer vision assisted machine learning based controller for the Stewart platform to track spatial objects

The present work aims to develop an object tracking controller for the Stewart platform using a computer vision-assisted machine learning-based approach. This research is divided into two modules. The first module focuses on the design of a motion controller for the Physik Instrumente (PI)-based Stewart platform. In contrast, the second module deals with the development of a machine-learning-based spatial object tracking algorithm by collecting information from the Zed 2 stereo vision system. Presently, simple feed-forward neural networks (NN) are used to predict the orientation of the top table of the platform. While training, the x, y, and z coordinates of the three-dimensional (3D) object, extracted from images, are used as the input to the NN. In contrast, the orientation information of the platform (that is, rotation about the x, y, and z-axes) is considered as the output from the network. The orientation information obtained from the network is fed to the inverse kinematics-based motion controller (module 1) to move the platform while tracking the object. After training, the optimised NN is used to track the continuously moving 3D object. The experimental results show that the developed NN-based controller has successfully tracked the moving spatial object with reasonably good accuracy.