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Precise relative positioning in machine swarms

Precise relative positioning in machine swarms. Motivation and Introduction IMU/GNSS and Vision integration Swarm Positioning Mobile Ad-Hoc Communication First Results Conclusion and Outlook. Outline. Motivation . Urban and alpine scenario

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Precise relative positioning in machine swarms

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  1. Precise relative positioning in machine swarms

  2. Motivation and Introduction IMU/GNSS and Vision integration Swarm Positioning Mobile Ad-Hoc Communication First Results Conclusion and Outlook Outline

  3. Motivation Urban and alpine scenario • Shadowing of GNSS signals, degradingGNSS signals, multipath effects • Guidance with the help of known landmarks is limited • Fast search with high accuracy positioning Integration of IMU/GNSS including Failure Detection and Exclusion Methods Vision-aided relative localization Swarm Positioning using GNSS raw data exchange Mobile Ad-hoc communication for GNSS raw data exchange

  4. Introduction – “NExt UAV” Joint research project “NExt UAV“ Institute of Flight Guidance Institute of Agricultural Machinery and Fluid Power Institute of Flight Systems Fundedby FKZ 50NA1002 and50NA1003

  5. Motivation and Introduction IMU/GNSS and Vision integration Swarm Positioning Mobile Ad-Hoc Communication First Results Conclusion and Outlook Outline

  6. IMU/GNSS and Vision integration - FDE • System architecture • Main filter processes all measurements (N) • Each subfilter processes (N-i) measurements (i = 1…N-1)

  7. IMU/GNSS and Vision integration – Vision based localization

  8. Correction Prediction IMU/GNSS and Vision integration - Coupling Monitoring INS/GNSS GNSS IMU Tight coupled Predicted Feature World Position Feature Pixel Position Predicted Pixel Position Feature World Position Vision

  9. Motivation and Introduction IMU/GNSS and Vision integration Swarm Positioning Mobile Ad-Hoc Communication First Results Conclusion and Outlook Outline

  10. Swarm Positioning - Standalone rover b rover a

  11. Swarm Positioning – Double Differential rover b rover a

  12. Swarm Positioning – Standalone & Double Differential rover b rover a rover c

  13. Motivation and Introduction IMU/GNSS and Vision integration Swarm Positioning Mobile Ad-Hoc Communication First Results Conclusion and Outlook Outline

  14. Mobile Ad-Hoc communication - Requirements • Quick and safe data exchange • Flexible for dynamic changes in network topology • Decentralized system to compensate loss of swarm participants • MANet or Mesh networks • Scenarios: All2All, All2One, One2All Without direct data linkDirect data linkMulti-hop data link

  15. Mobile Ad-Hoc communication - Simulation • Using MATLAB® • Proactive routing – All2All • 4 to 12 nodes • 1000 simulations • Steps: • Generate random network • Network discovery • Routing • Data Exchange

  16. Mobile Ad-Hoc communication - Implementation • Network exploration • Calculating the routing table • GNSS raw data exchange • Data processing

  17. Mobile Ad-Hoc communication – Network Exploration • Problem: No a-priori knowledge and no coordinator  Try and Error • Tools: Clear Channel Assessment (CCA) • Scan energy level of channel • Compare detected energy with threshold • If medium not clear wait random backoff time and try again • Problems: • Hidden stations • No ACK available using broadcast messages

  18. Mobile Ad-Hoc communication – Network Exploration

  19. Motivation and Introduction IMU/GNSS and Vision integration Swarm Positioning Mobile Ad-Hoc Communication First Results Conclusion and Outlook Outline

  20. First Results – Reference systems • Reference localization system (Leica Viva TS15 ) • tracking with up to 6 Hz • compare track with GNSS/INS solution • Sync by GNSS-time using WLAN and NTP • Phase solution in post-processing UAV 2 UAV 1 Ref-Station UAV 3

  21. First Results - Measurements

  22. Motivation and Introduction IMU/GNSS and Vision integration Swarm Positioning Mobile Ad-Hoc Communication First Results Conclusion and Outlook Outline

  23. Conclusion and Outlook • Absolute and relative position is indispensable • Positioning techniques and controlling strategies requireswarm communication • Algorithm for network exploration fits simulation results • Test of all sub-systems together in one system • Tests in different scenarios (urban, alpine, different constellations) • Optimization of the required time  minimization of the required messages

  24. Thank you for your attention! Institute of Agricultural Machinery and Fluid Power www.tu-braunschweig.de/ilf ilf@tu-braunschweig.de comRoBS Dipl.-Ing. Jan Schattenberg J.Schattenberg@tu-braunschweig.de Tel.: +49 (0) 531 391-7192 Fax: +49 (0) 531 391-5951

  25. NExt UAV - IMU 2x dual axis MEMS acceleration sensor (Bosch SMB225) 3x 1-axis gyro sensor (Bosch SMG074 )1x “read-out”-board layout and design by IFF NExt UAV - GNSS µblox LEA-6T-chip (Precision Timing & Raw Data) Hybrid GPS/SBAS engine (WAAS, EGNOS, MSAS) Basis-Board layout and design by messWERK Technical Equipment

  26. Technical Equipment NExt UAV- NAV-Board • Seco Qseven™ Embedded Computer Module • Pico ITX-Standard (3,9”x2,0”, 100 x 72 mm) • Intel Atom (Z530) 1,6 GHz, 1 GB DDR2, 800 Hz bus • 8 GB Flashdrive, microSD-Slot (8 GB), SD-Slot (8 GB) • Operating system: linux with real time extension NExt UAV- Radio module • Wireless standard 802.15.4 XBee • Frequency Band 2,4 GHz ISM • Radio range up to 1.6 km • Serial Data Range up to 115.2 Kbps

  27. Technical Equipment NExt UAV- Vision • 2x AlliedVisionTec Marlin F-131B with Pentax C815B • Self-made carrier for stereo camera system • Interface: IEEE 1394a - 400 Mb/s • Resolution: 1280 x 1024 • 25 fps on full resolution NExt UAV- Vision-Board • Lippert CXR-GS45 PCI104Ex with Core2Duo 9300 • 2 GB Ram • Firewire expansion board

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