Improving Orchard Efficiency with Autonomous Utility Vehicles

Improving Orchard Efficiency with Autonomous Utility Vehicles

Brad Hamner, Sanjiv Singh, and Marcel Bergerman

Carnegie Mellon University

Comprehensive Automation for Specialty Crops2

Orchard Tasks Today

Maintenance tasks require humans to drive vehicles a large distance over a long time


Reconfigurable Mobility

• One type of vehicle can perform multiple tasks

– Mowing, spraying, etc.

– In use more often

• One automation package can be applied to multiple vehicles

– Only one system to learn

4Comprehensive Automation for Specialty Crops

Autonomous Prime Mover

• Automated Toro eWorkman

• Demonstrate orchard tasksby towing otherequipment


Autonomous Prime Mover

• Laser range sensors

– Find trees

– Find obstacles

• Steering angle sensor

• Distance traveled sensor

• Controlled by laptop


Seeing the Trees, Finding the Row

• From laser sensors,robot sees one slice of the world

• Need to find tree rows from this data

– Two parallel lines


Line Representation

Lines represented by their perpendicularto the vehicle

distance d, angle

d

Tree point


Hough Transform Row Detection

• Hough Transform – Popular line detector in image processing

• Create “votes” for lines

• Looking for parallel lines with most votes


Hough Transform

• Pick a point in the image

How about this one?


Hough Transform

• Many lines could go through that point.Draw them, and record their d,

Distance

Angle

1

1

1

1


Hough Transform

• Pick another point. Draw its vote lines,and record their d,

Distance

Angle

1

2

1

1

1

1

1


Hough Transform

• Pick another point and repeat.

Distance

Angle

1

1

3

1

1

1

1

1

1

1


Hough Transform

• And repeat.

Distance

Angle

1

1

4

1 1

1 1

1

1

1 1

1

1


Hough Transform

• Repeat for all points.Two lines dominate the voting.

Distance

Angle

1 1 1

1 2 1 1 1 1

9 9

1 1 1 1 2

1 1 1 1 1

2 1 1

1 1 3 1

1 1 1 1

2 1 2 2

1 1 1 1 1 1


Hough Transform

• Find the top two peaks in the matrix.Those are the tree rows.

Distance

Angle

600

500

400

300

200

100

Votes

Actual Hough Matrix


Row Following

• Hough matrix peaks are the tree rows

• Drive midway between the two


Turning

• We know roughly how wide rows are

• Plan a path into the next row

• As the vehicle gets close, run row detection


Results

• Over 100 km autonomous travel

– Orchards in PA and WA

• Demonstrations

– Mowing

– Spraying

– Crop scouting


Results

• 10 km with an autonomous platform


Ongoing Work

• Uneven terrain brings the ground into view

– Presents data that looks like tree rows

– Confuses Hough detector


Ongoing Work

• Particle filter

– Tracks possible rows over time

– Rejects bad rows

– More stable results

Houghalone

ParticleFilter


Ongoing Work

• Not always much room to turn around

• Detect the available space

• Plan smooth trajectories

• Back up if necessary


Related Collaboration

• Comprehensive Automation for Specialty Crops– http://www.frc.ri.cmu.edu/casc/CASC/Welcome.html

• Inexpensive localization

– Improve tracking, build maps

• Information management

• Autonomous disease detection

• Extension and usability

– User interface

– Commercialization studies

– Cost benefit analysis


Acknowledgments

USDA Specialty Crop Research Initiative

Owners and managers ofSoergel Orchards

Penn State Fruit Research and Extension Center

Bear Mountain Orchards

Hollabaugh Orchards

Sunrise Orchards

Valley Fruit Orchards

Improving Orchard Efficiency with Autonomous Utility Vehicles

Education

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