Distributed Path Planning Classification with Web-based 3D Visualization using Deep Neural Network for Internet of Robotic Things

Abstract

Internet of Robotic Things (IoRT) distributes heterogeneous intelligences among devices and platforms. A distributed control of a three-degree-of-freedom (3-DOF) robot manipulator is integrated with web-based 3D visualization. An asynchronous protocol was utilized to broadcast kinematic data of a 3-DOF robot manipulator between platforms. However, kinematic data computed using inverse kinematic equations directly cannot identify the singularity issue of robot manipulator. Singularity avoidance required to prevent robot component or joint from damage. Therefore, this study proposed a deep neural network approach as a classification-based of manipulator robot path planning to avoid singularity issues. Deep neural network (DNN) was trained in 12 minutes, 52 seconds in 500 iterations. Training accuracy measured with value 96,23 percent, validation accuracy measured with value 96,13 percent, and testing accuracy measured with value 96,48 percent Additionally, 3 DOF manipulator robot web-based 3D visualization was made using Web Graphics Library (WebGL). The distributed platform was tested successfully and can distribute and classify 2352 motions per second.