Descripción del proyecto
Robótica para la recuperación de materiales reciclables
La recuperación de materiales reciclables suele realizarse de forma manual en instalaciones de valorización de materiales (MRF, por sus siglas en inglés) ubicadas en las proximidades de zonas urbanas densamente pobladas. Los recientes avances en materia de inteligencia artificial (IA) y robótica permiten la automatización de varias actividades de estas MRF. Sin embargo, estas técnicas no resultan rentables. Tampoco es adecuado para grandes volúmenes de residuos ni para superficies pequeñas. Aunque las unidades portátiles de recuperación de materiales surgen como respuesta a estas últimas, no están exentas de limitaciones. El equipo del proyecto RECLAIM, financiado con fondos europeos, creará una MRF robótica portátil (prMRF) adaptada a la recuperación de materiales a pequeña escala mediante el aprovechamiento de tecnología totalmente demostrada de robótica, IA y análisis de datos. En el proyecto se adopta un planteamiento modular de múltiples robots y pinzas para la recuperación de materiales y un enfoque de ciencia ciudadana.
Objetivo
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.
Ámbito científico
- engineering and technologyenvironmental engineeringwaste managementwaste treatment processesrecycling
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- social sciencespolitical sciencespolitical policiescivil society
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
- social scienceseconomics and businesseconomicssustainable economy
Palabras clave
Programa(s)
Convocatoria de propuestas
HORIZON-CL4-2021-DIGITAL-EMERGING-01
Consulte otros proyectos de esta convocatoriaRégimen de financiación
HORIZON-IA - HORIZON Innovation ActionsCoordinador
70013 Irakleio
Grecia