With the advancement of technology, the adopted processes need to accompany this advance. The main objective of the changes seeks to meet continuous improvement, in order to improve working conditions and the satisfaction of employees who work at the factory, reducing the risks to human health and safety. The processes of recycling waste electrical and electronic equipment are composed of numerous dangerous, monotone, and time-consuming tasks that must be replaced. It is here that automatic processes based on robotics appear as a solution. Through the robotics solutions proposed in the HR-Recycler project, workers and robots will collaborate in a synchronized manner. With this, the risk to the health and safety of workers due to the handling of potentially hazardous waste will be reduced and workers will be able to focus on tasks raising the level of quality, value, and qualification. And as a result, the companies will be able to increase their recycling rates.
It was expected to have collected images between partners Sadako and Interecycling. But unfortunately, with some delays and the difficult situation of COVID-19 that is affecting the whole world, this stage is still to be accomplished. We are hopeful that this whole scenario will soon improve rapidly in order to complete this stage.
Regarding the recordings, we will collect data for the Classification step in the same way as we have done in the previous recording campaigns, in the other end-users partners, Indumetal and Bianatt. For the Disassembly step, we will record hand-held camera data, later used for the co-bot operation, as well as whole-body images used for action recognition software.
The recordings will not be fundamentally different from those made previously, in the end-users mentioned above. In order to improve and to make better the results already obtained, we can try some calibration of the camera for the disassembly step and use the images later at Sadako to assess operational performance rather than only to build neural networks. This camera calibration would allow us to better locate the operator in space and use all the developed software functionalities.
So with this stage and recordings, we hope to gather data to improve our existing neural networks, both for the Classification and Disassembly steps, and to use some of this data to measure our operational performance. To get to know a little better the work developed in the area of image collection you can consult the blog post of SDK from last 26/06/2020 (https://www.hr-recycler.eu/blog/).
Ana Catarina, INT
Albert Tissot, SDK