A modular and extensible Python port of the TUD-AMR MPC Planner, designed for real-time motion planning under constraints. PyMPC brings together math utilities, solver-agnostic optimization backends (e.g. CasADi, OSQP), and a flexible constraint interface to support rapid prototyping and deployment of Model Predictive Control systems.

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🚀 Features

• Model Predictive Control (MPC) framework in Python
• Modular design for swapping solvers or models
• Support for:
  • State/input constraints
  • Soft and hard constraints
  • Obstacle avoidance
• Built-in math utilities for dynamics, linearization, etc.
• Modification of the original C++ codebase
• Unit-test friendly structure for rapid development

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🛠 Installation

git clone https://github.com/stephen-crawford/PyMPC.git
cd PyMPC
pip install -e .
Requires Python 3.8+, NumPy, and optionally CasADi or OSQP depending on your backend.

Related Work This library is a Python port of the excellent tud-amr/mpc_planner, originally written in C++. Our goal is to preserve its structure and intent while providing a more flexible, Pythonic interface for rapid development and experimentation.

Contributing Contributions welcome! If you'd like to add models, constraints, or solver support, open a pull request or issue.

MIT License