Periodic Reporting for period 2 - RECLAIM (AI-powered Robotic Material Recovery in a Box)
Période du rapport: 2024-03-01 au 2025-08-31
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 develops a portable, robotic MRF (prMRF) tailored to small-scale material recovery. The project 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 retraining 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.
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 develops a portable, robotic MRF (prMRF) tailored to small-scale material recovery. The project 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 retraining 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.
The RECLAIM project has successfully delivered a portable robotic Material Recovery Facility (prMRF) capable of efficiently sorting recyclable waste in small, remote, or otherwise hard-to-access regions. By overcoming the dependence on large centralized waste management infrastructures, the project has demonstrated a cost-effective, scalable, and rapidly deployable solution suited to local material recovery operations.
Throughout RECLAIM, proven advances in robotics, computer vision, and artificial intelligence were not only leveraged but significantly refined and integrated into a cohesive industrial-grade system. The resulting prMRF combines robustness, mobility, and a high degree of automation, establishing a new benchmark for decentralized and sustainable waste processing.
The final project report summarizes the completed activities and validated outcomes of the project, highlighting the progress achieved particularly in the second half of its duration, during which efforts concentrated on system-level integration, optimization, and real-world testing.
Throughout RECLAIM, proven advances in robotics, computer vision, and artificial intelligence were not only leveraged but significantly refined and integrated into a cohesive industrial-grade system. The resulting prMRF combines robustness, mobility, and a high degree of automation, establishing a new benchmark for decentralized and sustainable waste processing.
The final project report summarizes the completed activities and validated outcomes of the project, highlighting the progress achieved particularly in the second half of its duration, during which efforts concentrated on system-level integration, optimization, and real-world testing.
RECLAIM introduced an innovative robotic solution known as the Robotic Recycling Worker (RoReWo), specifically engineered for efficient and cost-effective waste picking and sorting.
RECLAIM developed a new method for integrating the results of RGB and HSI waste categorization, which enables highly accurate waste classification, thereby increasing the purity of recovered materials.
RECLAIM developed an early version of the globally unique portable, robotic Material Recovery Facility (prMRF) that is tailored to decentralized, small-scale material recovery.
RECLAIM created an initial version of the Recycling Data Game, which aims to (i) raise social awareness about the circular economy and (ii) enhance the collected waste data with user feedback and annotations, as a means to improve AI algorithms.
RECLAIM developed a new method for integrating the results of RGB and HSI waste categorization, which enables highly accurate waste classification, thereby increasing the purity of recovered materials.
RECLAIM developed an early version of the globally unique portable, robotic Material Recovery Facility (prMRF) that is tailored to decentralized, small-scale material recovery.
RECLAIM created an initial version of the Recycling Data Game, which aims to (i) raise social awareness about the circular economy and (ii) enhance the collected waste data with user feedback and annotations, as a means to improve AI algorithms.