Periodic Reporting for period 1 - RECLAIM (AI-powered Robotic Material Recovery in a Box)
Reporting period: 2022-09-01 to 2024-02-29
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.
In the current project implementation period, early experiments have been carried out that examine the technologies planned to be used in the project and confirm to a significant extent their functionality and efficiency.
In particular, starting from the technologies to be used in the portable, robotic, Material Recovery Facility (prMRF), experimental tests were carried out that confirm the possibility of using deep learning networks for the identification of recyclable objects on a moving belt, based on visual features. Similar experiments have been conducted with respect to the use of hyperspectral imaging and their ability to separate similar material types (ie. plastics).
Experiments were also carried out that confirm that the option of using low-cost linear robots to sort objects into different collection bins is valid and has high potential to increase the "pick per invested euro" rate in robotic material recovery solutions.
The above enabling technologies have been installed inside the container hosting the prMRF. As of early 2024, all components are in place and functional. Experiments have focused on testing the relevant technologies in pairs or subgroups to ensure proper operation. Full scale experiments have also been carried out, showcasing the prMRF's capacity to facilitate material recovery in remote and distant areas.
In parallel with the progress on the creation of the prMPF there has been significant progress on the implementation of the Recycling Data Games (RDGs). A first version of the RDG is already available and has been internally tested by the beneficiaries involved in the project.
In particular, starting from the technologies to be used in the portable, robotic, Material Recovery Facility (prMRF), experimental tests were carried out that confirm the possibility of using deep learning networks for the identification of recyclable objects on a moving belt, based on visual features. Similar experiments have been conducted with respect to the use of hyperspectral imaging and their ability to separate similar material types (ie. plastics).
Experiments were also carried out that confirm that the option of using low-cost linear robots to sort objects into different collection bins is valid and has high potential to increase the "pick per invested euro" rate in robotic material recovery solutions.
The above enabling technologies have been installed inside the container hosting the prMRF. As of early 2024, all components are in place and functional. Experiments have focused on testing the relevant technologies in pairs or subgroups to ensure proper operation. Full scale experiments have also been carried out, showcasing the prMRF's capacity to facilitate material recovery in remote and distant areas.
In parallel with the progress on the creation of the prMPF there has been significant progress on the implementation of the Recycling Data Games (RDGs). A first version of the RDG is already available and has been internally tested by the beneficiaries involved in the project.
RECLAIM has introduced an innovative robotic solution known as the Robotic Recycling Worker (RoReWo), specifically engineered for efficient and cost-effective waste picking and sorting.
RECLAIM has developed an early version of the globally unique portable, robotic Material Recovery Facility (prMRF) that is tailored to decentralized, small-scale material recovery.
RECLAIM has 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 has developed an early version of the globally unique portable, robotic Material Recovery Facility (prMRF) that is tailored to decentralized, small-scale material recovery.
RECLAIM has 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.