EUROPEAN DRONE SUMMIT UAV DACH BRIDGING THE GAP BETWEEN UNMANNED SYSTEMS AND EUROPEAN INDUSTRY 15. OKTOBER 2018 / FRANKFURT / #EDS2018

Transcription

1 EUROPEAN DRONE SUMMIT UAV DACH BRIDGING THE GAP BETWEEN UNMANNED SYSTEMS AND EUROPEAN INDUSTRY 15. OKTOBER 2018 / FRANKFURT / #EDS :30: Session A.1: UAS for Geodesy, Monitoring and Inspections Dr. Andreas Hausotter Information gathering with different kind of automated and integrated drones 1

2 Agenda: Information gathering with different kind of automated and integrated drones what the customer wants? different types of drones for data gathering legal situation regarding BVLOS in Europe (SORA / UTM) examples for drone integration within risk management systems in critical infrastructures information gathering options in the future - automated anomaly detection by ai - deep learning & other technologies 2

3 What the customer wants regarding information gathering? Buying and flying a drone? may be only for some time Charging batteries and drone inspection? No! Buying images & videos from drone flying services? Yes, but Looking at drone images & videos for hours? No! Like customers buying processed information as a service? YES!! 3

4 What the customer wants? - different types of drones for data gathering Overall target: Supply of tailor-made information Specification of payload: Definition of drone type: 4

5 detailed 4-phase-approach of a customized solution Definition of the technical possibilities of flying drones Specific analysis of the customer s situation, as well as application of our technological solutions Analysis of the legal situation of drone flights and the customer at risk Organizational optimization and integration using modern security concepts Analysis of the consequences for the employees Proposals for external and internal communication of the concept Integration of our drone flight solutions into existing control station control of drones (GPS spoofing) in airspace Deployment assistance, using Resource analysis, and Establishment of project management staff training Phase 3: Phase 4: Phase1: feasibility study Phase 2: organisational and legal due dilligence technical feasibility implementation 5

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7 legal situation regarding BVLOS in Europe Current legal European situation: most drones fall under national drone rules (GER: Drohnen VO) BVLOS: EU-wide rules are under discussion including ICAO, JARUS & FAA; -> goal: safely operate drones (technical & operational - SORA) UTM (Unmanned Traffic Management): concept to meet BVLOS requirements & EU-rules; making sure to share the skies safely by true drone integration into already existing airspace (ATM) Current status (Mid 2018): EU-wide rules for safety of drones approved by European parliament EASA - Next steps The EU Commission will discuss the regulation proposed by EASA. The adoption is expected by the end of

8 overview EASA new safety categories (SORA classification) (Specific Operational Risk Assessment) For the specific category, the operator needs an authorisation after a satisfactory risk assessment is made. OPEN: Low risk Without involvement of Aviation Authority Limitations ( Visual line of sight, Maximum Altitude, distance from airport and sensitive zones) Flight over Populated area is possible if: No overflying of crowds Industry standards (Case of toy of less than 500 g) SPECIFIC Increased risk Safety risk assessment Approved by NAA possibly supported by Qualified Entities unless approved operator with privilege Operation Authorisation with operations manual Concept of accredited body Airworthiness of drone and competence of staff based on risk assessment CERTIFIED Comparable to manned aviation Limit between specific and certified is not yet defined Pending criteria are defined, EASA accept application in its present remit TC, C of A, Noise certificate, Approved Organisations, licences (Case of small drones) Command and Control and Detect & Avoid can receive an independent approval 8

9 escdrones security technology in the airspace by mobile video surveillance MSAP (Multi-Service-Aerial-Platform) FMS Flight-Management-System automated flying Navigation (GNSS) Collision Avoidance ADS-B Sensors - electro Optical - thermal - rangefinder - radar / Lidar Telemetrie / Realtime Datalink by 24/7 airspace surveillance Counter UAS Sensors: RF scanning Radar doppler or FMCW Electro optical Acoustical Effectors: narrow & wideband smart jamming: FHSS, WiFi (2,4 + 5,8 GHz) GNSS Spoofing (GPS, ) HPEM Sensorfusion perimeter protection microwave sensors alarm sensors lightning sensors escdrone Managementsystem: Civil Air Surveillance System with - Integrated UAV Command and Control Function (C&C) - Counter UAS (CUAS) Corporate Security Management: Risk Management System (RMS) 9

10 Kernfunktionen moderner Drohnensysteme Automatisiertes Fliegen (vor-definierte Szenarien, Abfliegen von Wegpunkten, take-off & landing) Klassifizierung der Daten durch intelligente Kameratechnik (Unterscheidung Situation A, B und C) Integriert in vorhandene Sicherheitsinfrastruktur / GMS (Gefahren-Management-System) und intuitive Bedienung des Systems Drohne als zugelassenes Luftfahrzeug (Safety features Flug & Daten) Der Einsatzzweck bestimmt den Drohnentyp (Multicopter, Helicopter, Flächendrohne batterieelektrisch, Verbrenner) Steigerung der Flugdauer, Größe zu Nutzlast, selbstständiges Laden (Batteriedichte) 10

11 Achievement with processed image & video recognition Decrease of false-alarm-rates Increase of usability Decrease of staff & operating expenses Reduction of routine activities Enhance the reliability of measurements Reduction of logistic costs, e.g. manned helicopters Affordability of aerial information More safety & security of operations Less hardware deployment 11

12 Future processing of digital images with automated anomaly detection of situations (persons / animals / objects) from the air Processing with artificial intelligence (ai) Reduction and merging of 3D-data information fusion & integration Utilize the most recent advances in machine-learning, computer vision and deep learning operations -> increased degree of automation Image processing with automatic object recognition Precondition for precise geo reference: Flight altitude, GPS-coordinates and RTK-information (real-timekinematic) Examples of automated anomaly detection: Differential detection of situation changes Recognition of foreign objects Recognition of movement patterns / face recognition (differ customer / burgler) improved analysis of security & environmental applications Other examples of processing: navigation, mapping, object identification/counting, object relative movement, etc. 12

13 Deep learning examples: 13

14 Deep learning examples: 14

15 Deep learning examples: 15

16 Deep learning examples: 16

17 Requirements: Large amount of training data increases the detection rate of objects & persons and minimizes false alarms Precision accuracy The algorithms are based on the recognition of features, which allows probability calculations to infer whether this is an object of a particular object class. Image-to-image referencing processes permit image stabilization in imager sequences, mosaicking and super resolution 17

18 Application examples: Improvement of automated security infrastructure (surveillance of large car parking areas) Automated surveillance of secure situations (break-in) Automated alerts Automated inspections (railway lines, pipelines, boarders, power lines, field observations) -> Important: automation of processing operations 18

19 Lines of Business Space Systems & Applications Cyber Security & Systems Full Service UAS Integrator 19

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