Optimum Path in Karting
TL;DR
A simple starting model to find the optimum path in karting.
Intro
Based on the gopros data and videos you can find online, you can see whats the G-circle model is all about.
See that generally a kart can accelerate up to: 0.3G
Brake up to: 1G
And turn up to: ~1G
Tinkering with Racing and GoPro GPS
NEW - Optimum Path RepoWe come from some previous tinkering on racing:
How to find the optimum path in karting?
This is a great moment to step back and look at the “Architecture” of your karting simulator.
You are moving from raw data to a high-level optimization problem.
In real life unlike (apparently) in modern economics if you assume that a sferic cow will fly - you will be laughed at (and fail quickly).
So these strategies to estimate that fastest lap have all HYPOTHESIS with pros/cons.
1. The Data Foundation (The Input)
- Source: You have NSP (Haltech) and CSV files containing track limits and G-limits.
- Hypothesis: The physical limits of the kart are constant (Max , Max ), but acceleration decreases as a function of speed: .
- Pros: CSV is universal and easy to manipulate in Python.
- Cons: NSP is proprietary; you’ll likely need to export it to CSV to make it “readable” for your script.
2. The Physics Models (The “Engine”)
We discussed two ways to treat the kart:
| Model | Hypothesis | Pros | Cons |
|---|---|---|---|
| Point Mass (G-G Circle) | The kart is a single dot. Grip is a perfect circle (or ellipse). | Very fast to calculate; requires very little data. | Ignores weight transfer, chassis flex, and tire “scrub.” |
| Bicycle Model | Accounts for CG height and weight transfer. | More realistic “yaw” (rotation) modeling. | Requires more measurements (CG height, wheelbase). |
3. The Path Strategies (The “Line”)
This is where the actual “lap time” is found.
Option A: Minimum Curvature Path (MCP)
- Hypothesis: The fastest way is the “smoothest” way (maximizing radius ).
- Pros: Mathematically elegant; very fast to solve; much better than the centerline.
- Cons: Ignores the “Late Apex” advantage. It treats a 10m straight and a 100m straight the same way.
Option B: Late Apex (Momentum/Time Bias)
- Hypothesis: Sacrificing mid-corner speed for a straighter exit wins time on long straights.
- Pros: This is how professional drivers actually drive.
- Cons: Harder to calculate with a simple formula; requires “looking ahead” at the track.
4. The Solver (The “Brain”)
Since you have an AMD 5600G, you have the power to choose how to find the best line:
The Genetic Algorithm (GA) — Recommended
How it works: Evolution. It tries 100 lines, keeps the 10 fastest, “mutates” them, and repeats.
Pros: * It will “discover” the late apex on its own because it only cares about the lowest time.
Easy to code in Python (
PyGADorNumPy).Perfect for your 5600G (you can run 12 “generations” at once).
Cons: Can take a few minutes to converge on the “perfect” lap.
Neural Networks / Reinforcement Learning
- How it works: An AI “agent” drives the kart millions of times to learn the limits.
- Pros: Can handle very complex physics (sliding, grass, etc.).
- Cons: Massive overkill for a point-mass model; very high “coding cost” to setup the environment.
Summary of My Recommendations for You:
- Format: Convert everything to CSV.
- Physics: Use the G-G Ellipse (allowing more braking Gs than acceleration Gs) combined with your ** decay function**.
- Pathing: Use a Genetic Algorithm.
- Use Splines (don’t optimize every point, just “control points”).
- Use Vectorization (NumPy) so your 5600G can calculate 1,000 laps per second.
- The Result: This will give you a “Theoretical Best” lap time that accounts for the specific top speed (85 km/h) and power drop-off of your kart.
Optimum Path v1 - The mass point model
#sudo apt install gh
gh auth login
#gh repo create make-podcast --private --source=. --remote=origin --push
git init && git add . && git commit -m "Initial commit: simple landing website" && gh repo create optimum-path --private --source=. --remote=origin --push