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Automated Turf Management
Head: Sanjiv Singh
Contact: Sanjiv Singh
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Project Homepage
Overview
To achieve complete automation on golf courses, we are developing capabilities for:
  • Reliable obstacle detection.
  • Must be able to find obstacles as small as a golf ball, while not generating false positives.
  • Also must detect all true obstacles to keep the vehicle safe.
  • Precise navigation. Must be able to operate with cm level precision to create the cross-hatch patterns seen on premier golf courses.
  • Effective coverage. Must be able to create patterns that cover the entire fairway in an efficient manner.

Benefits
The key benefits of this technology are

  • reduction in reliance on skilled operators, and
  • expanded hours of golf course availability.
Automated mowing has enormous further commercial potential, since it would be potentially attractive to any lawn owner.

Approach
The first approach to obstacle detection is

  • color segmentation, which is the ability to distinguish obstacles based on color. We are developing a method that is robust to changes in color due to shadows and atmospheric conditions.
The second approach we are investigating is
  • stereo based homography, to detect objects which lie above the ground plane.
We are also using a
  • SickTM Laser scanner as a backup detection system, for instances where vision-based methods fail.

Status

  • As of December 2000, we have implemented path tracking and obstacle detection on the mower. We are doing runs of over 1 km in length. The obstacle detection system has been enhanced with the addition of a Sick laser sensor, for use in validating obstacles in situations where vision cannot perform.
  • In August 2000, we held a successful demonstration of this system in which we performed over 50 iterations of our demonstration. Each iteration consisted of mowing six rows, each 10 meters long.