Authors:
Jaeseok Kim
;
Olivia Nocentini
;
Marco Scafuro
;
Raffaele Limosani
;
Alessandro Manzi
;
Paolo Dario
and
Filippo Cavallo
Affiliation:
The Biorobotics Institute, Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, Pontedera, Pisa and Italy
Keyword(s):
Image Processing, Manipulation, Grasping, Deep Learning, Classification of Materials, Recycling System.
Related
Ontology
Subjects/Areas/Topics:
Industrial Automation and Robotics
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Performance Evaluation and Optimization
Abstract:
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 c
omputed. 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
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