Project description
Growing AI and robotics solutions to cut labour shortages
In critical sectors such as renewable energy and agriculture, there is a challenge in sustaining competitiveness amid escalating labour shortages. To safeguard vital industries, a seismic shift toward AI and robotics integration becomes imperative. In this context, the EU-funded ARISE project will introduce cutting-edge technologies that revolutionise complex manipulation tasks and redefine the future of automation. Specifically, it will develop reconfigurable pneumatic manipulators, variable-stiffness soft end-effectors, and cutting-edge perception modules. This innovative toolchain, with applications in installation, repair, transplanting, and harvesting tasks in solar and hydroponic farms, promises a transformative leap in the automation landscape. Overall, the project’s comprehensive approach, from hierarchical imitation learning to edge-AI deployment, heralds a new era of efficiency and progress.
Objective
Maintaining the competitiveness and leadership position of crucial industries such as renewable energy and agriculture, is contingent upon AI and Robotics increasingly becoming a widespread and integral part of the relevant technological landscapes, particularly in the face of steep labour shortages. ARISE project aims to introduce a combination of perception and control modules around a reconfigurable robotic manipulator that will enable a step change in the level of automation of complex manipulation tasks. ARISE will comprise the following key novel technology components that will significantly push the state of the art in terms of automatic task segmentation, human robot interaction and complex manipulation: (1) Two reconfigurable pneumatic-based robotic manipulators mounted on a mobile robotic platform (2) Novel soft end-effectors with variable stiffness that will allow for a diverse set of manipulation tasks (3) A Robotic perception module comprising 3D vision algorithms (4) A Localisation and Mapping module that will allow the robot to accurately identify its position within the environment (5) A Semantic Mapping module powered by scene understanding algorithms (6) A Knowledge Representation framework that will capture important information regarding objects’ properties and relationships (7) A Hierarchical Imitation Learning framework for acquiring robotic skills to accomplish complex tasks directly from human demonstrators (8) A Human-Interaction conditioned Path and Task planning module enabling reactive robot control (9) An edge-AI framework for deploying Machine Learning models and computer vision algorithms at the edge in a streamlined fashion. The ARISE toolchain will be integrated and validated in 5 real use case scenarios including installation and repair, and transplanting and harvesting tasks, in solar and hydroponic farms, respectively.
Fields of science
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- social sciencessociologyindustrial relationsautomation
- natural sciencescomputer and information sciencesknowledge engineering
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
15232 Chalandri
Greece