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27/04/2020

CERTH explain their work in AI-enabled Cell-level perception methods

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CERTH explains their work in AI-enabled Cell-level perception methods.

The objective of this task is to provide the envisaged robot with the ability to perceive the industrial environment so as to effectively collaborate with the humans and assist them with the Waste Electrical and Electronic (WEEE) device recycling process. Towards this direction, CERTH recorded multiple recycling procedures of four WEEE devices, namely PC Towers, Microwave Ovens, Flat Panel Displays and Emergency Lamps, focusing on the human-robot collaboration aspect during the WEEE device disassembly, thus creating a WEEE device component detection dataset by manually annotating a number of the recorded frames.

Given this dataset, CERTH deployed state of the art Computer Vision methods utilizing Deep Learning techniques in order to train a Convolutional Neural Network (CNN) capable of detecting the four WEEE devices as well as their respective components during the various stages of the disassembly process. Additionally, CERTH enhanced these methods by proposing an anchoring mechanism targeting specifically the detection of small objects.

GAIKER and real world scenarios within HR-Recycler

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GAIKER goes on with the definition of the real-world scenarios in which the novel WEEE recycling approach based on human-robot collaborations will be demonstrated and validated

GAIKER continues the progress in the task of outlining the scenarios of the pilot demonstrations, whose ultimate aims are to test and evaluate, in real operating environments, the performance of the solutions developed within the HR-Recycler Project to manage WEEE (Waste Electric and Electronic Equipment). This advance is supported by the regular interaction with the partners of the Consortium that, as evidenced after the 1st Project Review Meeting held in Brussels (Belgium) at the end of January 2020, are doing significant and consistent progresses in the development of hardware and software solutions.

In the definition of scenarios, GAIKER is considering how to integrate all the relevant technological advances generated during the implementation of the scheduled tasks, involving user-cases definition, factory-level modelling, cell-level perception, collaborative schemes definition and robotic equipment design and programming. GAIKER is also striving to keep in close contact with the other project partners to understand, update and gather any relevant information that can help in the definition of more precise, realistic and functional pilot scenarios.

Until now, the greatest contributions have been:
– The definition of the use-cases and current recycling scenarios by the industrial WEEE recyclers involved in the HR-Recycler.
– The initial modelling of the factory floor and human workers, including work cell conceptual configuration, virtual factory 3D simulations and process orchestration.
– The demonstrated capabilities of advanced artificial intelligence-based perception in terms of automatic object recognition and manipulation, and human motion prediction.
– The preliminary definition of the robotic equipment, as articulated arms and automated vehicles, and the software for controlling robotic motion and interactions.
– The establishment of foundations for human-robot safe collaboration and adaptative interaction.

GAIKER has been thoughtfully registering, analyzing and integrating all these advances, particularly focusing on those aspects most closely linked to the key performance indicators defined to evaluate the HR-Recycler system. Thus, GAIKER has identified the main elements of the scenarios, including work cells and plant floor layouts, robot and worker movements (sifting and manipulation), detailed procedure requirements, material flows, human-robot interactions, safety issues and process orchestration. Based on these data GAIKER has begun to build the conceptual model for the pilot scenarios. This is, naturally, a flexible and additive model and GAIKER will be aware of any further novelty and progress in the projected tasks.