Model Predictive Control and Trajectory Planning for Quadrupedal Locomotion

This repository contains solution to the final assignments for the Legged Robots lecture (CIT436000) at TU Munich.

This project focuses on the development and evaluation of a motion planning and control system for legged robots, specifically addressing the challenges of generating stable and dynamic locomotion. The project leverages a combination of Model Predictive Control (MPC) and trajectory planning to achieve coordinated movements between the robot's base body and its individual legs. The framework has been implemented within the MuJoCo simulation environment and simulated for the robot Go2 Quadruped Robot by Unitree Robotics.

The detailed report for the project can be found in the final_project.ipynb.

Installation

1. Clone this repository:

2. Create and activate a Conda environment from the provided YAML file:

   conda env create -f LeggedRobotsGP.yml
   conda activate LeggedRobotsGP

3. Ensure MuJoCo is correctly installed on your system.

The Project Structure

├── model/                       # Directory for robot model files (Unitree Go2)
├── project_code/                # Main project code directory
│   ├── controller/              # Robot joints controller
│   ├── planner/                 # Full body and foot position planners
│   ├── state/                   # State management for the robot
│   ├── utils/
│   └── constants.py             # Constants for the project
├── .gitignore             
├── final_project.ipynb          # Complete project demonstration aka the report
├── LeggedRobotsGP.yml           
└── MPC_Example.ipynb            # MPC use case demonstrations