faster r-cnn and 3d reconstruction for handling tasks implementing a scara robot

This paper presents the design and results of using a deep learning algorithm for robotic manipulation in object handling tasks in a virtual industrial environment. the simulation tool used is v-rep and the environment corresponds to a production line based on a conveyor belt and a scara type robot manipulator. the main contribution of this work focuses on the integration of a depth camera located on the robot and the computation of the gripping coordinates by identifying and locating three different types of objects of interest with random locations on the conveyor belt, through a faster r-cnn. the results show that the system manages to perform the indicated activities, obtaining a classification accuracy of 97.4% and a mean average precision of 0.93, which allowed a correct detection and manipulation of the objects.