A CNN-Based Fast Picking Method for WEEE Recycling

Abstract

Recycling of Waste Electrical and Electronic Equipment (WEEE) is challenging due to the high variety in both the design and composition of the products in this waste stream. Therefore, targeted demanufacturing and appropriate material sorting processes are crucial, but remain often a manual and thus labor-intensive and costly activity. Despite first steps towards the automation of these sorting processes can be noticed, many of them are in material sorting, but very few in the sorting at product level. In this perspective, one of the key tasks is the automated classification, picking and manipulation of WEEE products.In the presented research, a closed-loop grasp planning method is proposed for the random picking of WEEE products. The presented grasping method combines a Convolutional Neural Network-based quality prediction and Closed-Loop control, named CNNB-CL, to find the optimized grasp region for unknown objects in a dense clutter. A large-scale dataset is generated for the CNN training, containing over 2.3 million synthetic grasps, and their corresponding grasp qualities evaluated by grasp simulations using 3D models. The proposed 7-layer light-weighted CNN quantitatively estimates the grasp quality of each grasp candidate. In addition, the CNNB-CL utilizes the feedback from a force-torque sensor to detect the optimized grasp and to adjust the grasp strategy. Various empirical experiments prove that the CNNB-CL method finds an optimized grasp for a WEEE clutter from 2,000 grasp candidates within 0.3 s and achieves an average success rate of 92% for different WEEE clutters. Furthermore, the average picking speed is as high as 1,400 mm/s for the random picking of small WEEE products (< 300 g) with a FANUC Delta robot (M-2i A 3SL). The fast-picking ability of the presented method reveals its potential application for the (pre-) processing of WEEE.