Motion Planning Skill

Sampling-based and optimization-based motion planning algorithms

509 stars

Best use case

Motion Planning Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sampling-based and optimization-based motion planning algorithms

Teams using Motion Planning Skill 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/motion-planning/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/robotics-simulation/skills/motion-planning/SKILL.md"

Manual Installation

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

How Motion Planning Skill Compares

Feature / Agent	Motion Planning Skill	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Sampling-based and optimization-based motion planning algorithms

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

# Motion Planning Skill

## Overview

Expert skill for implementing and configuring motion planning algorithms, including sampling-based planners (OMPL) and optimization-based trajectory planners.

## Capabilities

- Configure OMPL planners (RRT, RRT*, RRT-Connect, PRM, FMT*)
- Implement hybrid A* for car-like robots
- Set up lattice-based planners
- Configure trajectory optimization (TrajOpt, CHOMP, STOMP)
- Implement time-optimal trajectory planning
- Set up path smoothing algorithms
- Configure state space and validity checking
- Implement kinodynamic planning
- Set up multi-query planning with roadmaps
- Configure asymptotically optimal planners

## Target Processes

- path-planning-algorithm.js
- trajectory-optimization.js
- moveit-manipulation-planning.js
- nav2-navigation-setup.js

## Dependencies

- OMPL (Open Motion Planning Library)
- MoveIt
- TrajOpt
- FCL (Flexible Collision Library)

## Usage Context

This skill is invoked when processes require path planning algorithm selection, trajectory optimization, or custom motion planning solutions.

## Output Artifacts

- OMPL planner configurations
- State space definitions
- Validity checker implementations
- Trajectory optimization setups
- Path smoothing configurations
- Planning benchmark results

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