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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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Presentation Transcript
outline
Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and Outlook

Outline
motivation
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

introduction next uav
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

outline1
Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and Outlook

Outline
imu gnss and vision integration fde
IMU/GNSS and Vision integration - FDE
  • System architecture
  • Main filter processes all measurements (N)
  • Each subfilter processes (N-i) measurements (i = 1…N-1)
imu gnss and vision integration coupling

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

outline2
Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and Outlook

Outline
outline3
Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and Outlook

Outline
slide14

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

slide15

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
slide16

Mobile Ad-Hoc communication - Implementation

  • Network exploration
  • Calculating the routing table
  • GNSS raw data exchange
  • Data processing
slide17

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
outline4
Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and Outlook

Outline
slide20

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

outline5
Motivation and Introduction

IMU/GNSS and Vision integration

Swarm Positioning

Mobile Ad-Hoc Communication

First Results

Conclusion and Outlook

Outline
slide23

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
thank you for your attention
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

technical equipment

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
technical equipment1
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
technical equipment2
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