The middle pillar of how we code: VEXcode teaches, Path Planner visualizes, the drive library competes. This is the "visualize" step — designing an autonomous route on a screen instead of guessing numbers in code.
Writing an autonomous routine as a wall of driveFor(24); turnTo(90); calls means guessing distances and angles, then re-running the robot over and over to tune them. A path planner lets you draw the route on a picture of the field, see it before you ever deploy, and export a file your code follows. It turns a blind tuning loop into a visual design problem.
Path planning is one stage in a pipeline. Each tool does one job well:
The path planner most V5RC teams use is PATH.JERRYIO — a free, web-based editor. It lets you design, preview, and simulate driving routes for skills and autonomous, then generate a path file the robot reads. If you have used a design tool like Figma, the click-and-drag interaction will feel familiar.
One detail that makes it click into V5RC cleanly: it uses the same coordinate system as the V5 GPS sensor — the field origin is at the center, Y increases toward north and X toward east, and a VRC field is 12 feet square with 2-foot tiles. So the coordinates you see in the planner match how the field is actually measured.
Path planning earns its place when a routine has several waypoints, curves, or a skills run where hand-tuned straight-line moves get tedious and brittle. For a two-move autonomous, plain coded movements are fine — learn those first on the first auton exercises. Reach for the planner once the route gets complex enough that seeing it beats guessing it.
It is also the natural tool for designing a one-minute driver-skills route, since you can lay out and share the whole run visually.
In the Spartan model, Path Planner is the visualize step: it bridges learning to compete. You learn movement in VEXcode, you see the route in the planner, and the drive library executes it. None of the three replaces the others — they hand off to each other. A programmer who can plan a path visually, then trust the library to follow it, spends tuning time on strategy instead of on guessing numbers.