Path Optimization

Path Optimization

The Path Optimizer uses a genetic algorithm to automatically refine your autonomous paths. It evolves your path over multiple generations to find the fastest route while strictly avoiding collisions.

Core Concepts

The optimizer treats your path like a living organism that evolves. It tries thousands of variations to balance two competing goals:

1. Speed: Minimizing travel time based on your robot’s constraints (velocity, acceleration).
2. Safety: Avoiding all defined obstacles and field boundaries.

Before You Start

For the optimizer to work effectively, ensure your Settings are accurate:

• Robot Dimensions: Set the correct Length and Width of your robot.
• Safety Margin: Add a buffer (e.g., 2 inches) to ensure you don’t graze obstacles. The more precise the drive train the smaller the buffer.
• Obstacles: Define accurate obstacles in the “Obstacles” tab.

The Optimization Process

When you run the optimizer, it follows these steps:

1. Initialization: It starts with your current path and creates 50+ variations (the “population”).
2. Evaluation: It simulates every path to calculate travel time and check for collisions.
3. Selection: It keeps the fastest, safest paths. Colliding paths are heavily penalized.
4. Mutation: It slightly modifies the survivors—moving control points or adding/removing them—to explore new solutions.
5. Repeat: This cycle repeats for the defined number of iterations.

Note: The optimizer will automatically add or remove control points if it helps navigate around an obstacle.

Understanding the Settings

You can tune the optimizer in the Settings menu. Here is what each knob does:

1. Optimization Iterations

• What it is: The number of generations the algorithm runs.
• Effect: Higher values give the optimizer more “time to think.”
• Recommendation: Start with 100. Increase to 300+ for complex obstacle courses.

2. Population Size

• What it is: The number of path variations tested in each generation.
• Effect: Larger populations explore more options but run slower.
• Recommendation: 50 is usually sufficient.

3. Mutation Rate

• What it is: The probability that a control point will be moved in a generation.
• Effect:
  • Low (0.1): Fine-tuning. Good for polishing a nearly perfect path.
  • High (0.5): Exploration. Good for finding completely new routes.
• Recommendation: Default is 0.4.

4. Mutation Strength

• What it is: The maximum distance (in inches) a point can move during a single mutation.
• Effect:
  • Low (1.0): Small nudges.
  • High (10.0): Large jumps.
• Recommendation: Default is 6.0. The optimizer automatically increases this if the path is currently colliding.

Pro Tips

• Locking: If you have a segment you know is perfect (e.g., scoring alignment), select the line and check Lock Line. The optimizer will not touch it.
• Good Start: Give the optimizer a rough path that is somewhat close to valid. It works faster if it doesn’t have to untangle a complete mess.
• Visual Feedback: The dashed line shows the best path found so far. If it stops improving, you can click Stop Optimization early.