Project Information

Students will work individually and in groups to implement an autonomous greenhouse agent. The agent will be built up through seven assignments and two deployments on your greenhouse. Students will submit write-ups about their algorithm choices and implementation to be graded in simulation and through a real world deployment. Deployments will also include a graded presentation and written report evaluating the deployment of the agent after the grow periods. Assignments will be submitted following the instructions on Canvas.

RELEASED	DUE DATE	DESCRIPTION
08/26	09/04	Agent Architectures and ROS: Gain familiarity with the Robot Operating System (ROS) and the behavioral and layered architectures
09/04	09/16	Finite State Machines: Implement behaviors using Finite State Machines and gain familiarity with the TerraBot hardware
09/16	09/27	Monitoring and Testing (Group): Add monitors to your agents, develop formal tests of the behaviors, and prepare your agents for the first grow period deployment
09/30	10/21	Machine Learning: Test different ML algorithms to predict changes in the TerraBot's behavior
10/21	11/04	Computer Vision & Grow B Prep (Group): Use computer vision techniques to detect foliage and estimate plant growth; Prepare your agent for Grow Period B
11/04	11/15	Explanations: Generate explanations for why behaviors (FSMs) did, or did not, operate as expected
11/15	12/02	Scheduling (Group): Augment your agent's planning layer to adapt the schedule based on observed conditions