Name: Model Predictive Control (MPC) Laboratory
Type: Organization
Bio: Our research lab focuses on the theoretical and real-time implementation aspects of constrained predictive model-based control
Location: UC Berkeley
Blog: http://www.mpc.berkeley.edu/
Model Predictive Control (MPC) Laboratory's Projects
Main branch for BARC related code
ROS2 package for interfacing with the BARC hardware. Also includes BARC firmware.
A simplified version of the MPC Lab codebase for teaching and project purposes
Berkeley Library for Optimization Modeling
Open-source simulator for autonomous driving research. Fork for MPC Lab.
ROS bridge for CARLA Simulator. Goes with fork of Carla simulator.
YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )
YOLO ROS: Real-Time Object Detection for ROS
Dragon Lake Parking Dataset by MPC Lab
MEng 2018 code for sensor fusion and control.
Messages Package for the Hyundai Genesis, UCB MPC Lab
Code for autonomous parking with the Hyundai Genesis.
MPC Path Follower for the Hyundai Genesis.
Implementation of Hierarchical MPC with RAID-Net
Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)
3D LiDAR Object Detection & Tracking using Euclidean Clustering, RANSAC, & Hungarian Algorithm
arXiv 2011.00413: (ROS and Python package for the experiments on BARC) Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach.
Autonomous parking model predictive control example code in Julia
C++ ROS2 packages that implement learning model predictive control for real-world autonomous race cars.
Code for uising the HTC vive tracking system with ROS2