SLAM: Suction, Localisation And Manipulation

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Recycleye has already developed a low-cost, AI-powered system replicating the power of human vision. It uses advanced machine learning algorithms to provide automatic, image-based detection of individual items in co-mingled waste streams, at a material and object level. It leverages a cutting-edge synthetic data generation pipeline, and Recycleye's own WasteNet - the world's largest visual database of labelled waste items.In a previously funded R&D project, Recycleye augmented the vision system with a robotic arm to deliver a robotic sorting solution. Now, Recycleye will develop and test a novel robotic grasping technology, that will drastically improve picking accuracy (and therefore the efficiency) of Recycleye's current commercial offerings, enabling the application of Recycleye's technology to waste streams beyond Municipal Solid Waste (MSW), whilst providing significant technical development for the wider industrial automation industry,The method currently used in automation industry when picking objects, is to aim for the centre of the detected object when grasping. This is optimal if the item is flat, however in numerous industrial applications items are crushed and deformed leading to a sub-optimal or failed item pick. By applying the latest computer vision algorithms and techniques to the grasping problem, Recycleye will develop the first optimal grasp prediction technology that can be deployed real-time to maintain accuracy in complex fast-paced industrial applications. This technology has a huge potential for boosting Recycleye's system capabilities within the waste industry, while providing a step forward in grasping technology relevant to a range of industries.