Post on 24-Aug-2020
ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Towards Agricultural Robotics for Organic Farming
Wels, May 11th-13th 2016
Georg Halmetschlager, Johann Prankl, Markus Vincze Vienna University Of Technology, ACIN, V4R gh@acin.tuwien.ac.at
ÖAGM/ARW
ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
FRANC v1 (2014)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
FRANC v2 (Now)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Field Robot for Advanced Navigation in bio-Crops • Example: manual weed control • Modular system design
• Autonomous platform • System extensions for different applications • → Avoid re-design of existing solutions
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BIO
-LU
TZ G
MBH
The Big Picture
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Robot system • Mechanical system • Electrical system • Sensors & software modules
• Navigation • Positioning of the robot
relative to the crop rows • Crop row detection • Challenges
• Different plant species, growing stages, field/row structures, plant densities
©
BIO
-LU
TZ G
MBH
The Big Picture
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Subsystems • Mechanical
• Reusability, flexibility • Electronics and Control System
• Modularity, replaceability • Row Guidance and Autonomy Software
• Robustness, replaceability, parameterization free, and GPS free
Modularity
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Frame Implement Powertrain
Mechanical Subsystem (1)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Mechanical Subsystem (2)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Example 4 WS → Ackermann steering principle?
Kinematics (1)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Linear Interpolation between • steering angles → Ackermann
constraint violated
• Interpolation of ICC → Ackermann constraint fulfilled.
Kinematics (2)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Electronics
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• SoA • Sensors [1-7]
• Cameras, LIDAR, laser range finder • Cost efficiency → pure machine vision approach
• Segmentation [1-5] • Color Information, spectral information, 3D information • Combination of NIR and 3D information
• Crop Line Detection [2-9] • Hough Transformation, Linear regression, stripe analysis, blob
analysis • Probabilistic methods
[1] Astrand, B., Baerveldt, (2005; [2] Jiang et al., (2010); [3] Ruiz, et al., (2010); [4] Romeo et al., (2012); [5] Kise, M., et al., (2005); [6] Weiss, U., Biber, P., (2009); [7] Fontaine, V., Crowe, T., (2006)
Sensor and Navigation System
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Sensorsystem • 3D Information Stereo
cameras
• Spectral Information NIR
camera
• NIRD Segmentation • Discriminate Plants from
Soil • Different growing stages • Different plant species • Combination of NIR and
3D Information
[www.theimagingsource.com/]
[www.ptgrey.com/]
Sensor System and Segmentation
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Row Detection (and Tracking) • Parallel lines can be represented by 3 parameters • Basevectors |r|, q, d establish a 3D parameter space • Generate hypotheses
• Based on given data • Sequential approach
Generic Crop Row Detection
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Search after parallel lines • RANSAC
• Hypotheses generation out of the given data-set • One-step algorithm
• Particle Filter • Verification of randomly generated hypotheses • Cyclical algorithm
Crop Row Detection
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Parallel line search • Grouping after parallelism • Grouping after offset • Offset based group reduction
RANSAC
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• 3D parameter space • Initialization with N random hypotheses • Rating of N hypotheses • Redrawing of N hypotheses
• Good hypotheses are selected several times (”survival of the fittest”)
• Prediction, followed by next iteration • Hypotheses clusters after some iterations
Particle Filter- Theory
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Prediction • Movement of the robot has to be modeled
Particle Filter- Prediction (1)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
• Assumption→ movement of the particles within the parameter space can be modeled with Gaussian noise
Particle Filter- Prediction (2)
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Particle Filter- Example
ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Results- Particle Filter
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Conclusion & Contributions
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• Mechanics • Solved problems with deadlocks during steering motion
• Electronics • Engineering • Safety remote control
• Machine Vision • Robust new segmentation method that combines strongest
features • Generic, GPS-free, probabilistic crop row detection and tracking
method that offers high detection rates • Stereo/NIR Dataset • ROS modules
ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
Conclusion & Contribution
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ARW/ÖAGM 12.05.2016
Georg Halmetschlager gh@acin.tuwien.ac.at
This work was funded by Sparkling Science a programme of the Federal Ministry of Science and Research of Austria (SPA 04/84 – FRANC).
Questions?
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