HR-Recycler aims at developing a hybrid human-robot collaborative environment for the disassembly of waste of electrical and electronic equipment. Within this environment, humans and robots will be working collaboratively by sharing different processing and manipulation tasks.
The HR-Recycler project will implement learning paradigms in virtual reality (VR) settings, for which a recycling plant will be modeled in 3D to be displayed in a VR environment. One objective is to capture accurately the interaction of the workers with the environment and especially the objects present in the factory. Another objective is to allow workers to practice disassembly procedures and to properly interact with the environment. It is necessary to deliver a VR environment as realistic as possible and to model the procedures so that they can lead to more efficient training of the workers.
Within the project DIGINEXT is developing an effective virtual training system, targeting the WEEE recycling industry. The authoring tool (Procedure Editor) aims at easing the creation of WEEE disassembly virtual training experiences by minimizing production efforts.
Unlike a generic 3D tool, DIGINEXT’s solution does not require any specific programming or 3D modeling skills and allows us to adopt a smoother and faster workflow as compared to classical solutions based on a 3D engine. This means that a field expert can create procedures for virtual training even with very limited programming skills.
The tool is used by following these simple steps:
The first step consists of 3D modeling and animation: setting the scene (building, furniture) and selecting the WEEE that will be recycled.
The 3D model is next split and each part that requires an operation (e.g. screws) has an action attributed (e.g. unscrew, open, etc.). The figure below shows the list of parts for a microwave oven, and for a specific screw it can be seen that it is a “screwable” part that has 2 possible actions “screw” and “unscrew” (circled in red).
The procedure is then constructed very simply by assembling boxes within the graphical environment, by attributing these actions in a linear way that follows the processing as done in the actual factories.
Once the procedure is finished it can be either played using the mouse (see picture below) or exported following the S1000D norm and be used e.g. for I.A. training by other members of the project’s consortium.
Michael Darques, DXT
