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SADAKO showcase their developed technologies!

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SADAKO explains their role in HR-Recycler and illustrates how their developed technologies and models are applied to various stages of the project.

As an AI and computer vision company, the role of Sadako in the HR-Recycler project is to provide the crucial vision algorithms to the robotic systems, allowing the perception of the environment in which the machines evolve.

Because of the wide variety of vision-dependent systems being employed in this project (different types of robot arms and Automatically Guided Vehicles, central factory planner), many different challenges are encountered which need each their own specific solution. All of the designed solutions rely on the use of RGB-D cameras and computers capable of Neural Network inference, and will be a good illustration of the many capabilities and applications of computer vision.

Until now, Sadako has developed solutions for the Classification and Disassembly stages of the recycling process, as well as for the AGV navigation.

Classification: 3D perception and object recognition for bin picking
Disassembly: Video-inference for gesture recognition, needed for Human-Robot collaboration and task status management
AGV: Human location perception, needed for optimal AGV path planning and human safety

  • Classification:

This stage is the first of the recycling process, where WEEE arrives mixed in baskets and has to be sorted by type of object (Microwave, Flat Panel Display, PC Tower, Emergency Lamp)
The goal in this stage of the process is to identify the type and 3D orientation of each object lying in the basket, and provide the robotic arm with a picking decision. This is achieved through the use of Deep Learning vision algorithms and point cloud processing

  • Disassembly:

The following process step, Disassembly, is where the previously sorted objects are dismantled and their parts are sorted by material to be sent to their final recycling location. The dismantling is made by an operator, with the support of a collaborative robotic arm. In order to achieve collaboration with the robot, Sadako has developed software based on Neural Networks capable of detecting actions in a video, in contrast with standard computer vision that detects objects in an image. This software is able to interpret the gestures being made by the operator, allowing him or her for example to stop or start the robot at any time. The software also allows the monitoring of the dismantling task’s status.

  • AGV:

AGVs are used to transport WEEE objects between each stage of the dismantling process, reducing high-strain and repetitive physical work for the operators. To ensure that the AGV trajectory does not interfere with the movement of humans around the factory, the central system has to be aware of their position. To achieve this, Sadako has developed a solution based on Openpose, a software that extracts the position of the joints of humans on an image, crossed with depth information provided by the camera, thus allowing to perceive the position and orientation of operators in three dimensions. This software can be deployed at the Disassembly workstation as well as onboard the AGVs.

In its compromise of developing technology to improve the world and reduce health risks endured by humans in the industry, Sadako is staying strongly active and working in the progress of the HR-Recycler project, despite the hard times imposed by the pandemic situation

From theory to practice!

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In two sessions held at the BIANATT  Aspropyrgos installations on July 8-9 and September 23-25, the procedures of dismantling FPD screens, desktop towers, emergency lighting, and microwave ovens, as well as the procedure of classifying WEEE appliances, were recorded in a special high power desktop computer using high-performance cameras.  Both sessions were coordinated and recorded by CERTH researchers.

BIANATT provided all the equipment and installed a customized construction to allow image recording from many different angles. Below you may see photos of the installation that was used for this purpose.

The recordings will be utilized by CERTH to produce Deep Learning models capable of detecting the aforementioned appliances as well as their internal parts during the disassembly process.