RECLAIM · AI-powered Robotic Material Recovery in a Box

Recyclable materials recovery is a key element of the circular economy and the EU Green Deal. It is typically performed manually at large scale Material Recovery Facilities (MRFs) installed close to dense urban areas. Recent advances in AI and robotics have enabled the automation of several MRF activities. However, they target large waste volumes and are not cost-effective for smaller, less accessible areas.To accommodate the latter, portable material recovery units can be deployed nearby. Despite the increasing demand for portable units, offerings lack intelligent, automated components that could significantly increase their productivity.To fill this gap, RECLAIM will develop a portable, robotic MRF (prMRF) tailored to small-scale material recovery. The proposal exploits well-tested technology in robotics, AI and data analytics which is improved to facilitate distributed material recovery.RECLAIM adopts a modular multi-robot/multi-gripper approach for material recovery, based on low cost Robotic Recycling Workers (RoReWos). An AI module combines imaging in the visual and infrared domain to identify, localize and categorize recyclables. The output of this module is used by a multi-RoReWo team that implements efficient and accurate material sorting. Further, a citizen science approach will increase social sensitivity to the Green Deal. This is accomplished via a novel Recycling Data-Game that enables and encourages citizens to participate in project RTD activities by providing annotations to be used in deep learning for the re-training of the AI module.RECLAIM developments will be implemented and repeatedly assessed in demanding, real material recovery tasks. Three different scenarios will attest its effectiveness and applicability in a broad range of locations that face material recovery challenges. This will pave the way for the prMRF market uptake and provide a major boost in making Europe zero polluting, climate-neutral, sustainable and globally competitive.