Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator

Abstract

Abstract In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.