Descrizione del progetto
Robotics use cases and Accompanying measures
The latest developments in robotics and the associated fields of computer vision and sensors open the floor for automated robotic solutions, exploitable in the near to medium term in the field of inspection of the civil infrastructure in general and transportation tunnel infrastructure in particular. The latter infrastructure is ageing urgently requiring inspection and assessment. Presently, inspection is mostly performed through tunnel wide visual observations by inspectors. This process is slow, labour intensive, expensive, subjective and often requiring lane shutdown during inspection at a time of limited budgets and inspector resources and heightened requirements for safety and maximum tunnel uptime. ROBINSPECT, driven by the tunnel inspection industry, adapts and integrates recent research results in intelligent control in robotics, computer vision tailored with semi-supervised and active continuous learning and sensing, in an innovative, integrated, robotic system that automatically scans the intrados for potential defects on the surface and detects and measures radial deformation in the cross-section, distance between parallel cracks, cracks and open joints that impact tunnel stability, with mm accuracies. This permits, in one pass, both the inspection and structural assessment of tunnels. Intelligent control and robotics tools are interwoven to set an automatic robotic arm manipulation and an autonomous vehicle navigation so as to minimize humans' interaction. This way, the structural condition and safety of a tunnel is assessed automatically, reliably and speedily. The initial dataset on tunnel defects is provided from case studies (e.g. from London Underground) to be used not only for transfer learning but also for the evaluation of the structural models. The robotic system is evaluated and benchmarked at the research infrastructure of tunnels of VSH, at three road tunnels of the Egnatia Motorway and sections of the railway tunnel of London Post Office.
Campo scientifico
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- natural sciencescomputer and information sciencesartificial intelligencemachine learningtransfer learning
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
Invito a presentare proposte
FP7-ICT-2013-10
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Meccanismo di finanziamento
CP - Collaborative project (generic)Coordinatore
106 82 ATHINA
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