Abstract
Course project implementing Model Predictive Control (MPC) for Unmanned Ground Vehicles (UGV) navigating unknown environments with obstacle avoidance. The system generates optimal waypoints and controls in real-time using perception-based waypoint generation.
System Model
The system uses a bicycle model with the following state and control variables:
- State: X = (X, Y, ψ, ẋ, ẏ, ψ̇) — position, heading, and velocities
- Control: U = (δ, V) — steering angle and velocity
- Dynamics: Derived using tire cornering stiffness and longitudinal stiffness
Waypoint Generation
Perception module that converts camera images into navigable waypoints:
- Canny Edge Detection: For obstacle boundary extraction
- Hough Line Transform: To detect drivable paths
- Grid mapping: Convert 2D waypoints to occupancy grid for MPC
MPC Formulation
- Cost function: Minimizes tracking error and control effort
- Constraints: Collision avoidance using safety margins with slack variables
- Solver: Sequential Quadratic Programming (SQP) via CasADi
- Horizon: Configurable prediction horizon for real-time performance
Results
Successfully demonstrated obstacle avoidance in static and dynamic environments with replanning frequency of 10 Hz. Tested in both PyBullet simulation and ROS/Gazebo environments on Husky robot platform.
Setup
Recommended: Create a separate conda environment
conda create -n FOR_Project python=3.8
conda activate FOR_Project
git clone https://github.com/prakrutk/MPC-for-UGV.git
cd MPC-for-UGV
pip install --upgrade pip
pip install -e .
pip install -r requirements.txtRun MPC: python3 dynamics/MPC.py
Run Waypoint Generation: python3 Waypoint_generation/Waypoint_new.py