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In the framework of this project you will be responsible for conceiving innovative robotic picking systems that can sort the different alloys of metal scrap, such as different aluminium and stainless steel alloys, manganese, copper, brass and lead.
As KU Leuven research group on Life Cycle Engineering (LCE) of the Department of Mechanical Engineering we have acquired significant experience in re- and demanufacturing, which includes reuse, repair, remanufacturing and recycling of various waste streams and the dismantling of products into their components or composing materials. In addition, the ACRO research group has acquired substantial experience in the field of industrial automation, vision and robotics. Through the close cooperation of both research groups we exploit this knowledge to support machine builders in the development of the next generation of processes for the recycling sector. Hence, we support recycling companies in the transition towards a more circular economy.
Description of the research topic
Within the European EIT KIC Raw Materials Upscaling project AUSOM (AUtomatic SOrting of mixed scrap Metals) KU Leuven will collaborate with the companies Redwave (AU), Spectral Industries (NL) and Galloo (BE) and the research institute Swerim (SW) to develop an automatic sorting technology for metal alloys based on laser induced breakdown spectroscopy (LIBS). KU Leuven will contribute to four main tasks:robotic picking, (deep learning) computer vision, life cycle costing and impact assessment, and an educational component. In the framework of this project you will be responsible for conceiving innovative robotic picking systems that can sort the different alloys of metal scrap, such as different aluminium and stainless steel alloys, manganese, copper, brass and lead. A major challenge concerns the high variation in the geometries of scrap metal and the velocity at which gripping systems need to operate for metal sorting to be economically viable. You will, therefore, develop innovative gripping solutions that can handle the high variation in geometries and allow the fast grasping of scrap metal. For these developments, the applicability will be explored of solutions that are either commercially available or developed in prior research, as well as novel conceptual ideas. In addition, you will investigate opportunities to increase the speed of grasping by conceiving and optimizing grasp planning algorithms. One of the identified opportunities for increasing the robustness and speed of grasping is to adapt the grasp planning based on information acquired from the computer vision and LIBS sensor, such as alloy type, information on alloy densities and the geometry of the object to be sorted.
You will be involved in the development of the entire system and the integration of the different system components throughout the project in close cooperation with an internationally renowned machine builder, sensor builder and recycling company.In addition, you will investigate opportunities to combine robotic sorting with innovative computer vision, LIBs and other sorting technologies Throughout the project, conceptual approaches will be combined with experimental validation of innovative automated processes on lab scale and at the scale of an industrial pilot.
Description of the vacancy