Presented at American Society of Agricultural and Biological Engineers Annual International Meeting Pittsburgh, Pennsylvania, June 20-23, 2010

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

2. Orchard Tasks Today Maintenance tasks require humans to drive vehicles a large distance over a long time

3. 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

4. Autonomous Prime Mover Automated Toro eWorkman Demonstrate orchard tasksby towing otherequipment

5. Autonomous Prime Mover Laser range sensors Find trees Find obstacles Steering angle sensor Distance traveled sensor Controlled by laptop

6. 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

7. 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

8. Line Representation Lines represented by their perpendicularto the vehicle distance d, angle q Tree point q d

9. Hough Transform Row Detection Hough Transform – Popular line detector in image processing Create “votes” for lines Looking for parallel lines with most votes

10. Hough Transform Pick a point in the image How about this one?

11. Hough Transform Many lines could go through that point.Draw them, and record their d,q Angle Distance

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

13. Hough Transform Pick another point and repeat. Angle Distance

14. Hough Transform And repeat. Angle Distance

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

16. 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

17. 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

18. Row Following Hough matrix peaks are the tree rows Drive midway between the two

19. Turning We know roughly how wide rows are Plan a path into the next row As the vehicle gets close, run row detection

21. Results Over 100 km autonomous travel Orchards in PA and WA Demonstrations Mowing Spraying Crop scouting

22. Results 10 km with an autonomous platform

23. Ongoing Work Uneven terrain brings the ground into view Presents data that looks like tree rows Confuses Hough detector

24. Ongoing Work Particle filter Tracks possible rows over time Rejects bad rows More stable results ParticleFilter Houghalone

25. Ongoing Work Not always much room to turn around Detect the available space Plan smooth trajectories Back up if necessary

26. 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

27. 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