sunkepie / CoNi-MPC

CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control

CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control

Introduction

CoNi-MPC, Cooperative Non-inertial Frame Based Model Predictive Control, is a control framework to control the quadrotor flying in a non-inertial frame using non-linear model predictive control. This repo contains the necessary code and dependencies to run it.

If you are interested in this project, you can find more on the project webpage:

• https://fast-fire.github.io/CoNi-MPC/

and there's a preprint version for this project on arXiv:

• https://arxiv.org/abs/2306.11259

If you find this project useful for your own research work, please cite it with:

@article{zhang2023coni,
  title={CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control},
  author={Zhang, Baozhe and Chen, Xinwei and Li, Zhehan and Beltrame, Giovanni and Xu, Chao and Gao, Fei and Cao, Yanjun},
  journal={arXiv preprint arXiv:2306.11259},
  year={2023}
}

Prerequisites

• ACADOtoolkit (https://acado.github.io/)

First, clone this repo

git clone https://github.com/zhangbaozhe/CoNi-MPC.git

After cloning the repo, there are some dependencies that you should install

Install gflags

sudo apt install libgflags-dev

Build

Before you proceed, you need to first go to directory coni_mpc/acado_model/ to check code of the MPC model (in ACADO) is successfully built. Please read the README file in that directory in advance.

After you install the dependencies, you can use

catkin_make -DCMAKE_BUILD_TYPE=Release

to build the packages

Sometimes, if you face some Python issues, you may add -DPYTHON_EXECUTABLE=/usr/bin/python3 to build.

Run

We provide a sample numerical simulation node for your reference. To start this simulation, run

source devel/setup.bash
roslaunch coni_mpc num_sim_non_one_point.launch r:=1.0 v:=1.0 w:=1.0

This will let the car run with a linear velocity of 1.0 m/s and an angular velocity of 1.0 rad/s. There will be two rviz panels indicating the relative frame and the world frame. The quadrotor (agent) will follow a point (-1.0 m, 0.0 m, 2.0 m) at the back of the car (target).

Credits

This project is inspired by the code presented in Davide Falanga, Philipp Foehn, Peng Lu, Davide Scaramuzza: PAMPC: Perception-Aware Model Predictive Control for Quadrotors, IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS), 2018.

We adopt some of the code in the rpg_mpc repo and make modifications. The files below are similar to those in rpg_mpc

• acado_model/quadrotor_model_thrustrates.cpp
• include/acado_mpc/mpc_params.h
• include/acado_mpc/mpc_wrapper.h
• include/acado_mpc/mpc_controller.h
• src/acado_mpc/mpc_wrapper.cpp
• src/acado_mpc/mpc_controller.cpp