An Innovative Automated Robotic System Based On Deep Learning Approach For Recycling Objects

In this paper, an industrial robotic recycling system that is able to grasp objects and sort them according to their materials is presented. The system architecture is composed of a robot manipulator with a multifunctional grasping tool, one platform, a depth and an RGB camera. The innovation of this work consists of integrating image processing, grasping, motion planning and object material classification to create a new automated recycling system framework. An efficient object recognition approach is presented that uses segmentation and finds grasping points to properly manipulate objects. A deep learning approach was also used with a modified LeNet model for waste objects classification, sorting them into two main classes: carton and plastic. Image processing and classification were integrated with motion planning that is used to move the robot with optimized trajectories. To evaluate the system, the success rate and the execution time for grasping and object classification were computed. In addition, the accuracy of the network model was evaluated. A total success rate of 86.09% and 90% was obtained for carton and plastic samples grasped using suction, while 86.67% and 78.57% using gripper. In addition, a classification accuracy of 96% was reached on test samples