Convolution Neural Network Based Human and Robot Collaboration for 3D Scene Perception

Abstract

The approach of algorithmic operation of the Convolutional Neural Network (ConvNet/CNN) is like that of Deep Learning. The algorithm receives input instruction as an image by assigning characteristics of learnable weights and preferences on all inputs (aspects/objects) to the image. These features enable scalable input processing and effective differentiation and assignment of input and output images. For pre-processing requirements, ConvNet uses significantly low attributes as compared to other algorithms. However, in a primitive approach, filters are hand-engineered having sufficient algorithmic training. The ConvNets possess other abilities to learn image filtering and its characteristics. In this paper, an analysis of robot visual reasoning (for pick and place) is conducted. This refers to reasoning the latent meaning of visual signals or indications for future robot actions from visual observations of an HRC scene. In this paper, projection matrix estimation is computed using 2D points to represent the figure plane and 3D points to represent scene detection for initiating pick and place operation. During simulation, equations are represented using a matrix format based on figure coordinates. The design also implements automatic calibration and accuracy for the robot workspace. In addition, vision-based management for the robot end effector is also conducted based on horizontal targets, and upright targets. However, the pick and the place are articulated in the experiment without visual control. In our results, analysis of joint tangential forces is computed for the three joints, Alink1, Blink2, and Clink3. The outcome of axial force variation of the three joints is conducted in their state of mobility. Finally, the result of torque acting on the three joints increases when the simulation time is increased to achieve optimum performance