path-planning

Trajectory planning and motion control algorithm development

509 stars

Best use case

path-planning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Trajectory planning and motion control algorithm development

Teams using path-planning should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/path-planning/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/automotive-engineering/skills/path-planning/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/path-planning/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How path-planning Compares

Feature / Agentpath-planningStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Trajectory planning and motion control algorithm development

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Path Planning Algorithm Skill

## Purpose
Enable trajectory planning and motion control algorithm development for autonomous driving applications including behavior planning and emergency maneuvers.

## Capabilities
- Behavior planning state machine design
- Trajectory optimization (polynomial, spline-based)
- Model Predictive Control (MPC) implementation
- Lattice planner implementation
- Collision checking algorithms
- Comfort and safety constraint handling
- Emergency maneuver planning
- Parking trajectory generation

## Usage Guidelines
- Design behavior planning for predictable driving patterns
- Optimize trajectories for comfort and efficiency
- Implement robust collision checking at all planning stages
- Handle edge cases and emergency situations
- Validate planning algorithms in simulation
- Document algorithm parameters and tuning

## Dependencies
- ROS/ROS2
- Apollo
- Autoware
- MATLAB/Simulink

## Process Integration
- ADA-002: Path Planning and Motion Control
- ADA-003: ADAS Feature Development
- ADA-004: Simulation and Virtual Validation

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