Building a Perception and Navigation Stack for Field Robots

A field robot is only as good as the perception and navigation stack underneath it. The same robot platform can succeed in one orchard and fail in another, and the difference is usually in how the software handles changing light, uneven terrain, and the long tail of edge cases that outdoor work produces. This course walks through the design choices that shape a working stack. You will work through written design exercises that mirror how an agricultural robotics team would plan a perception and navigation pipeline. The emphasis is on the practical tradeoffs that decide whether a robot is reliable enough to run unattended for hours. What you'll learn: - Choose sensor configurations including cameras, depth sensors, and multispectral imaging for specific tasks - Design perception pipelines that recognize crops, weeds, and obstacles under varying outdoor conditions - Apply modern computer vision approaches including segmentation, detection, and on-device inference - Build a localization stack that combines satellite positioning, wheel odometry, and visual cues - Plan path-following and row-following behaviors for tractors, weeders, and scouts - Develop fallback behaviors and safe-stop logic for situations the robot cannot handle confidently The course progresses from sensors and perception through localization and finally to motion planning and safety. A capstone written exercise asks you to draft a one-page design for a perception and navigation stack for a robot of your choice. This course is designed for beginners with some background in software or general engineering, including computer science students, mechatronics learners, and agricultural engineers. No robotics-specific prior experience is needed. The course treats every system as a design problem you can reason about on paper before any code is written.