摘要
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.
