Improving Orchard Efficiency with Autonomous Utility Vehicles

Improving Orchard Efficiency with Autonomous Utility Vehicles
Brad Hamner, Sanjiv Singh, and Marcel Bergerman
Carnegie Mellon University

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

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 q
Tree point
q
d

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,q
Angle
Distance

Hough Transform
Pick another point. Draw its vote lines,and record their d,q
Angle
Distance

Hough Transform
Pick another point and repeat.
Angle
Distance

Hough Transform
And repeat.
Angle
Distance

Hough Transform
Repeat for all points.Two lines dominate the voting.
Angle
Distance

Hough Transform
Find the top two peaks in the matrix.Those are the tree rows.
600
Actual Hough Matrix
500
400
Angle
300
200
100
Distance
Votes

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
ParticleFilter
Houghalone

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

by Comprehensive Automation for Specialty Crops

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Improving Orchard Efficiency with Autonomous Utility Vehicles Presentation Transcript