Objective
THE PROBLEM
The inspection of underground infrastructure is done by operators using expensive machinery, with associated high costs (within the range 15 - 60 k€/km), high risks and safety issues. Unmanned Aerial Vehicles can replace inspection teams in these demanding tasks, reducing the cost by 90%, but no product in the market is able to operate autonomously in GNSS-denied environments and to produce reliable 3D mapping of the sites, sending high quality information to the system/person in charge of taking decisions.
AUTOFLYMAP SOLUTION
HS has developed the first drone able to fly autonomously in underground environments (GNSS-denied) and capable to produce high quality 3D textured models for mapping and inspection of indoor scenarios. The solution is applicable in several sectors requiring eventual or regular inspection of underground sites without the need of operator’s presence at visual distance: e.g. tunnel construction, underground infrastructure inspection and maintenance, rail and road operation, facilities management, mining, etc. This breakthrough solution is achievable thanks to the development of two proprietary technological solutions: 1) indoor multi-sensor-based positioning system; 2) high-performance on-board data gathering sensors sets and raw data processing solution for 3D mapping.
THE FEASIBILITY STUDY
HS will develop a detailed technical and business feasibility analysis, to assess the details of the service to be provided (procedures, data encryption, user interfaces, sensors update and functionalities of the flying robot), to quantify the achievable markets and to refine the Business Model to be implemented in the Market Entry phase. The targets are: i) Cost of Production (Robot Bill-Of-Materials) < 6 k€; ii) Service model confirmed at > 6 k€/km; iii) Letters of Intent signed with one international distributor; iv) 2 end users selected for the demo tests planned for Phase 2.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social scienceseconomics and businessbusiness and managementbusiness models
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robotsdrones
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energyhydroelectricity
- natural sciencescomputer and information sciencesdata sciencedata processing
Programme(s)
Funding Scheme
SME-1 - SME instrument phase 1Coordinator
28034 Madrid
Spain
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.