CarrotBot is a research platform for the Autonomow team. The first task of CarrotBot is to gather data for machine vision/machine learning to test 1) Can a machine tell the difference between a weed and a plant? and 2) Can we train a machine to do this automatically? The first version of CarrotBot will have a high-speed machine vision camera and a high-resolution camera for visual data, and a panning LIDAR system for sub-milimeter depth measurement. Encoders on the drive motors and RTK-GPS will measure precision position and velocity. Assuming we validate the weed detection system, the next step for CarrotBot is a development and test platform for the weed kill system (still TBD, but looking at different methods such as clove oil, high pressure steam/water, or lasers).
Right now we're about half-way finished with the chassis, building the sensor suite and data aquisition system. The goal is to deploy CarrotBot this Wednesday (2/9) in the fields in Avenal, CA.
None of this would be possible without the enormous help and vision (no pun intended) of Morgan Quigley, PhD candidate in the Stanford Artificial Intelligence Lab, researching robotics and machine learning. His work can be seen here:
http://xenon.stanford.edu/~mquigley/doku.php
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