Route Planning | Spartan Design VRC

// Section 01

Match Autonomous vs Skills — Different Games

Most teams plan their match autonomous and skills autonomous using the same approach. That is a mistake. They have fundamentally different objectives, constraints, and success criteria.

    
  Match vs Skills route comparison
  
  Match Auton (15s)
  
  Alliance side
  
  Opponent side
  
  path stays
  alliance side
  short, safe, alliance half only
  
  Skills (60s)
  
  full field
  longer path, full field, no opponent

Match Autonomous (15 sec)

Objective: win the autonomous bonus AND earn the AWP. Both can go to both alliances in the same match.

Risk profile: reliability over maximum score. A 100% reliable 10-point auton beats a 70% reliable 18-point auton in a 12-match qualifier.

Partner dependency: your route must account for what your alliance partner is doing. Coordination prevents collisions and element conflicts.

Field state: only your half of the field is accessible without crossing the autonomous line. Opponent robot is a real variable.

Success metric: win points + autonomous win point + consistency across all 12 qualification matches.

Skills Autonomous (60 sec)

Objective: maximum raw score in one run. No AWP. No bonus for winning — pure point accumulation.

Risk profile: maximize expected score per run. A higher-risk route that occasionally scores 120 is better than a safe route that always scores 60, if you have 3 attempts per event.

Partner dependency: none. You own the entire field. Route the full 12′×12′ square.

Field state: fully known and static. All elements start in documented positions. No opponent robot. Completely predictable.

Success metric: highest single score across your best run at any event this season.

💡

The most important distinction: in match autonomous, you are competing against the other alliance and cooperating with your partner. In skills autonomous, you are competing against a fixed field and your own previous best score. These require completely different mental models when planning.

Current Season Context — Push Back (2025–26)

VRC Push Back: 12×12 ft field, 15-second autonomous period, 1:45 driver control, 30-second endgame.

Autonomous: robots must not contact the opponent’s starting tiles or field elements that begin in the opponent’s zone
AWP conditions are published in the game manual — read them at kickoff, they change every season
Plan around the field’s actual scoring positions, not estimated ones

Skills autonomous: teams may start in any legal red alliance position and have 60 seconds to score independently.

No alliance partner — the entire field score is yours alone
Starting position is fixed for the entire skills run; pick the one that optimizes your route
Replanning between skills attempts is allowed — treat each run as a test cycle

Each team gets up to three driving skills matches and three autonomous coding skills matches. This means each event gives you three attempts to set your best skills score — plan accordingly.

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// Section 02

Designing a Match Autonomous Route

Match autonomous has a 15-second window and a 10-point swing on the line. Route design starts with what you must do (AWP), then adds what you can reliably do in the remaining time.

Step 1 — Know the AWP Conditions First

The AWP (Autonomous Win Point) requires completing specific tasks published in the game manual each season.

Read the exact AWP conditions — they change every year
The AWP is separate from your autonomous score — you can earn it without winning the autonomous bonus
Both alliance robots must meet the AWP conditions for the alliance to earn it

What exactly must be done to earn the AWP this season?
Can your robot accomplish the AWP task alone, or does it require your alliance partner?
Does the AWP task conflict with scoring elements (same game piece, same zone)?

Your baseline autonomous must always complete the AWP condition at 95%+ reliability. Everything else is bonus.

Step 2 — Map Your Half of the Field

Match autonomous boundary rules: robots may not contact opponent’s starting tiles, field elements starting in the opponent’s zone, or game objects in protected zones.

Check the game manual — specific zones and constraints change every season
Design your route to avoid boundary violations even if your robot drifts slightly
Buffer margins matter: don’t plan a route that passes within 2 inches of a restricted zone

⚙️ Route Planning Canvas — Sketch Your Autonomous

Starting position

Game elements

Goals / scoring zones

Autonomous line

Click and drag on the canvas to sketch a route. Double-click to clear.

Step 3 — Build Your Route Tier by Tier

Think of your match autonomous in three tiers, from safest to most aggressive:

Tier 1 — Baseline (100% reliability target): AWP condition only. If the field is unusual, if your partner has a conflicting routine, or if anything feels off during setup — this is what you run. It must never fail.
Tier 2 — Standard (90%+ reliability target): AWP + maximum scoring on your half. This is your typical qualification match routine. Tested exhaustively on your practice field.
Tier 3 — Maximum (for elimination matches only): everything in Tier 2 plus a high-risk element — crossing to partner side, a difficult second AWP element, coordinated two-robot sequence. Only run when the match situation requires maximum autonomous points.

Step 4 — Account for Your Alliance Partner

Before every match, confirm with your partner:

Which side of the field each robot handles
Who owns the AWP task (if it only needs one robot)
Does either robot cross the center? If yes, at what time and along which path?
What is the Tier 1 fallback if their autonomous fails?

⚠️

Two robots attempting to interact with the same game element during autonomous is one of the most common failure modes in VRC. If your 15-second window involves elements near the center line, map both robots’ routes on paper before the match and confirm there are no spatial conflicts.

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// Section 03

Designing a Skills Autonomous Route

Skills autonomous gives you 60 seconds, the entire field, a completely predictable starting state, and three attempts per event. Route design here is an optimization problem — maximize points per second of robot movement.

The Skills Route Planning Process

List every scorable element and its point value. Skills scoring can differ from match scoring — always check the game manual appendix for the current season’s skills-specific rules. Some elements are worth more in skills, some zones are scored differently.
Calculate points per second for each element. A 5-point element that takes 4 seconds to score is worth 1.25 pts/sec. A 3-point element that takes 1 second to score is worth 3.0 pts/sec. Prioritize high pts/sec actions. Low-value elements far from your route are often not worth the travel time.
Minimize travel distance between scoring actions. Map all targets on a field diagram and find the shortest path that visits the highest-value ones. This is a version of the Traveling Salesman Problem — you do not need to solve it mathematically, but thinking about it explicitly prevents routes that backtrack unnecessarily across the field.
Group elements by zone. Score elements in clusters rather than bouncing between opposite sides of the field. Grouping cuts travel time and makes the route more robust to small positional errors.
Plan the end-game last. Many games have a high-value end-game action (climbing, parking, stacking). Plan your route to arrive at the end-game position with 5–10 seconds remaining, not just 1–2.

Points-Per-Second Thinking

Action	Points	Est. Time	Pts/sec	Priority
Score near element (close to goal)	3	1.5s	2.0	High
Score far element (travel required)	3	4.0s	0.75	Low
End-game action (climb/park)	Variable	3–8s	Depends	High if worth >15 pts
Control zone bonus	Bonus	—	Free if on route	Always include
Element far off optimal route	Any	+6s detour	Very low	Usually skip

🏆

The field is completely known and static in skills. Unlike match autonomous where the opponent robot, preloads, and partner position introduce uncertainty, skills starts identically every run. This means you can invest in a longer, more complex route because there are no surprise variables — only your robot’s mechanical consistency is the limiting factor.

The Three-Attempt Strategy

With three runs per event, you can be strategic about which routes you attempt:

Run 1: execute your current best-tested route. Establishes your baseline score for the event.
Run 2: if Run 1 succeeded cleanly, attempt a slightly more aggressive version — one additional element, a faster path. If Run 1 had a failure, diagnose and fix before running the same route again.
Run 3: if you have a lead in skills ranking, run your most reliable route to defend it. If you need a higher score to qualify for states, attempt your maximum-point route — the risk is worth it when the alternative is not qualifying.

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// Section 04

Paper Planning Before You Code

The best autonomous routines are planned on paper first. Coding a route you have not thought through wastes time debugging problems that should have been caught at the planning stage.

📤 Route Planning Checklist

Printed top-down field diagram ready

Use official VEX layout from the game manual appendix

Start position defined precisely (corner, angle, wall distance)

Not 'near the tile' — exact measurements

Every action step labeled: drive, turn, intake, deposit

With estimated time for each step

Total route time calculated and fits in the time window

15s match or 60s skills — add 10% buffer

Route pushed by hand — path physically verified

Before writing any code

AWP conditions checked and incorporated if applicable

Read the exact season game manual conditions

Route documented in notebook with labeled diagram

Start position, path, action points, timing

    
  Paper planning process — field diagram with path
  
  field printout
  
  start
  
  goal
  
  action
  zones
  
  start
  pos.
  
  sketch path on paper before opening Onshape — know your route before you code it

📝

Rule: no autonomous code gets written until the route has been walked through physically in the pit and mapped on paper. Teams that skip this step spend hours debugging movements that would have been obviously wrong on a field diagram.

The Five-Step Paper Planning Workflow

1. Print or Draw the Field

Use a printed top-down field diagram (published by VEX in the game manual appendix) to plan on paper before touching the code.

Draw the robot start position, path, and all action points with a pencil — not a pen, so you can iterate
Label each step: drive X inches, turn Y degrees, deposit, intake
Calculate rough timing (at typical autonomous speed) and confirm it fits in the time window

2. Mark Your Starting Position Precisely

Starting position precision is critical — small errors compound through the entire route.

Define start position as a specific corner, orientation angle, and distance from the nearest wall — not "near the left tile"
Use tape markers or field perimeter walls to set position consistently every time
Document the exact start position in the notebook with a labeled diagram

3. Draw the Route as a Sequence of Waypoints

Draw the robot’s center path from start to first element, to goal, to next element, etc. Label each waypoint with:

The distance from the previous point (in inches)
The turn angle at that point (in degrees, and direction)
What mechanism action happens there (intake on, arm up, eject)
The expected elapsed time at that waypoint

4. Walk the Route Physically

Before writing any code, push the robot along the planned route by hand in the pit.

Does the path physically fit? Are there field elements or walls in the way?
Does the robot need to turn? Where are the action points relative to the scoring targets?
Fix path problems on paper — before they become code problems

5. Calculate the Time Budget

Time-budget your route before coding it.

Estimate each movement: drive 24in ≈ 1.5s, 90° turn ≈ 0.8s, intake action ≈ 0.5s
Sum all actions — compare to your available window (15s for match, 60s for skills)
If the route exceeds the window on paper, simplify now — not after writing 200 lines of code

✅

Paper planning is engineering notebook content. A field diagram with your planned route, waypoint labels, and timing annotations is exactly the kind of quantitative documentation that earns Design Award points. Take photos of your planning sheets and include them with your testing data in the notebook.

The Route Card

After finalizing a route, create a “route card” — a single index card or laminated sheet that the drive team keeps at the field:

Example Route Card
Route: LEFT — AWP + 2 elements
Start: Left tile, back-left corner, facing field center (0°)
Selector: Position 2 on Brain screen
Steps: Drive 24" → Turn 90°R → Score → AWP touch → Backup 12"
End: Robot at AWP line, ~12.5s elapsed
Fallback: If partner blocks AWP → select Position 1 (AWP only)

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// Section 05

PATH.JERRYIO — Visual Route Editor

PATH.JERRYIO is a free browser-based path editor specifically built for VRC. Draw routes on a to-scale field diagram, export them as code or waypoint files, and visualize your skills routes before writing a single line of EZ Template code.

ℹ️

PATH.JERRYIO is at path.jerryio.com — free, no account required, works in any browser including on your phone. It is a Progressive Web App that is installable and can be used without an internet connection.

What PATH.JERRYIO Does

Visual route drawing — click and drag waypoints on a scaled field image. The path is shown as a curve between waypoints, with heading indicators.
Bézier curve paths — drag handles to create smooth curves rather than sharp angle turns. Curved paths are faster and smoother for robots with odometry.
Code generation — it can generate code in various formats. Drivers can design and visualize skills routes with ease, share them with the team, and practice with confidence.
Skills route visualization — particularly useful for planning the full 60-second skills route. You can see the entire path overlaid on the field and identify where the route backtracks or has inefficient segments.

How to Use It for EZ Template

PATH.JERRYIO works best for teams with odometry (position tracking) — it outputs X/Y coordinates that assume the robot knows its position on the field. For EZ Template teams without odometry, its primary value is as a planning visualization tool rather than a code generator:

Open PATH.JERRYIO and select the current season’s field image
Place your starting position and draw the route by clicking waypoints
Read off the distances and turn angles from the waypoints — these translate directly to pid_drive_set() and pid_turn_set() values in your EZ Template code
Use the field overlay to check that your route avoids field elements and fits within the autonomous line boundary
Share the route image with your partner to coordinate the match autonomous

💡

Coordinate system: PATH.JERRYIO uses (0,0) as the center of the field. This makes it easy to tell where the robot is based on the sign of the x and y coordinates. When reading distances for EZ Template, calculate the distance between each waypoint pair, not the absolute coordinates.

For Teams With Odometry — LemLib + PATH.JERRYIO

Teams running LemLib can use PATH.JERRYIO to generate smooth odometry-based paths visually.

Draw the path on a virtual field, export to C++ code, import into your LemLib project
Only use PATH.JERRYIO if your team has working odometry — it cannot fix unreliable positioning
For EZ Template teams: use chassis.drive_distance() and chassis.turn_angle() with sensor stops — simpler and more maintainable

This is beyond the scope of most middle school teams using EZ Template, but it is the direction the most competitive high school teams move toward as they advance.

⚠️

PATH.JERRYIO generates planning data — not a complete autonomous routine. You still need to write the mechanism control (intake, arm, claw), handle the AWP condition, and implement chained movements and concurrent actions. The tool handles the navigation geometry; your code handles everything else.

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// Section 06

Field Variables That Break Planned Routes

A route that runs perfectly in your gym often fails at a competition field. Understanding why — and designing routes that are robust to field variation — is the difference between a team that consistently wins autonomous and one that wins at home.

The Six Major Field Variables

1. Tile Surface Consistency

VRC foam tiles vary between fields. Older tiles are more compressed, producing less friction. Brand-new tiles have slightly different texture. Your robot’s drive PID was tuned on your gym’s tiles — at competition, those same values may produce a different distance. Mitigation: test on your competition field’s tiles during the practice window before the event. If driving long or short, adjust the target distance in code before the first match.

2. Tile Seams and Field Assembly Gaps

Where two tiles meet, there is a small seam that can deflect the drive slightly. Competition fields are assembled by volunteers and the seams are not always perfectly aligned. A route that crosses many tile seams accumulates small heading errors. Mitigation: use the IMU for heading correction (EZ Template does this automatically). Run your autonomous on the event field during practice time and verify the ending position.

3. Starting Position Precision

If your robot starts 0.5 inches further left than intended, a movement that was supposed to end at a goal may miss it by 0.5–1 inch at the end of a 36-inch drive. For tight scoring sequences, this matters. Mitigation: use a physical reference — robot back corner against the tile edge, robot side against the perimeter wall. Develop a consistent pre-match placement ritual and practice it.

4. Game Element Position Tolerance

Game elements are placed manually before each match. The game manual specifies their starting positions, but human placement has tolerance. An element that should be at a specific coordinate may be 0.25–0.5 inches off. Mitigation: design your intake approach with a wider collection window. Drive slightly past the element’s expected position if your intake can catch it on the way. Never build a route that requires hitting a precise element location with no margin.

5. Battery Voltage

Drive PID performance changes with battery voltage. A 12.0V battery produces different motor output than a 12.5V battery at the same power command. Early in the day with a fresh battery, your robot may drive slightly faster and overshoot. Late in the day or after many runs, a lower battery may cause undershoot. Mitigation: fully charge batteries before every skills run. Run autonomous only on batteries at 12.4V or above. Consider adding a battery voltage check at the start of autonomous and adjusting drive speed accordingly.

6. IMU Calibration Location and Orientation

The IMU must calibrate while the robot is completely stationary. If the robot is placed on the field and then bumped or moved after calibration, heading will be off. Competition venues also sometimes have vibration from other matches running nearby. Mitigation: calibrate the IMU as the last step before the match starts. In EZ Template, chassis.initialize() calls IMU calibration automatically in the initialize() function. Verify the IMU shows calibrated on the Brain screen before stepping back from the robot.

Route Robustness Principles

Wider collection windows — approach game elements at an angle that allows collection even with 1–2 inch positional error
Wall touches for re-zeroing — in skills, drive into a field wall or goal structure to mechanically reset position before a precision sequence. Odometry is then re-zeroed to the known contact point.
Shorter individual movements — error accumulates with distance. A 10-inch drive accumulates less error than a 36-inch drive. Breaking long routes into shorter segments with heading corrections between each reduces error accumulation.
Heading checks after turns — EZ Template’s IMU heading correction after each turn is automatic. Do not skip the IMU or disable heading PID to save time — it is one of the most valuable consistency tools in EZ Template.

🏆

Always test your autonomous on the actual competition field during the practice window before your first match. Most competitions open fields 30–60 minutes before matches begin. Use every minute of that time. One successful run on the competition field is worth 10 successful runs in your gym.

⚙ STEM Highlight Mathematics: Optimization & Graph Theory

Skills route planning is a variant of the Travelling Salesman Problem (TSP) — find the shortest path that visits all high-value scoring locations. TSP is NP-hard in general, but the small number of VRC targets (typically 10–20) makes it tractable. For match autonomous, it is a constraint satisfaction problem: maximize score subject to the 15-second time budget and the autonomous line constraint. PATH.JERRYIO visualizes Bézier curves — parametric curves defined by control points — which provide smooth, drivable paths between waypoints.

🎤 Interview line: “Skills route optimization is a variant of the Travelling Salesman Problem — we want to visit the highest-value scoring locations in minimum time. We solve it heuristically by grouping targets by zone and minimizing backtracking. For match autonomous, it is a constrained optimization: maximize expected score in 15 seconds.”

🔬 Check for Understanding

You have 3 seconds remaining in autonomous and two scoring options: Action A (2 pts, 2.5s) and Action B (3 pts, 3.5s). Which should you attempt?

Action B — it scores more points

Neither — with only 3 seconds left, attempting either risks a timing failure mid-action

Action A — it fits within the remaining 3 seconds; B would not complete before time expires

Both simultaneously using concurrent actions

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