kilachi/CarND-MPC-Project

CarND Term 2 Model Predictive Control (MPC) Project

CarND-Controls-MPC

Self-Driving Car Engineer Nanodegree Program

Note:

This repo has been modified by olala7846@gmail.com, for the original repo, please reference Udacity's github

---

Dependencies

• cmake >= 3.5
• All OSes: click here for installation instructions
• make >= 4.1
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - [install Xcode command line tools]((https://developer.apple.com/xcode/features/)
  • Windows: recommend using MinGW
• uWebSockets
  • Run either install-mac.sh or install-ubuntu.sh.
  • If you install from source, checkout to commit e94b6e1, i.e.

    git clone https://github.com/uWebSockets/uWebSockets
    cd uWebSockets
    git checkout e94b6e1
    

    Some function signatures have changed in v0.14.x. See this PR for more details.
• Fortran Compiler
  • Mac: brew install gcc (might not be required)
  • Linux: sudo apt-get install gfortran. Additionall you have also have to install gcc and g++, sudo apt-get install gcc g++. Look in this Dockerfile for more info.
• Ipopt
  • Mac: brew install ipopt
  • Linux
    • You will need a version of Ipopt 3.12.1 or higher. The version available through apt-get is 3.11.x. If you can get that version to work great but if not there's a script install_ipopt.sh that will install Ipopt. You just need to download the source from the Ipopt releases page or the Github releases page.
    • Then call install_ipopt.sh with the source directory as the first argument, ex: bash install_ipopt.sh Ipopt-3.12.1.
  • Windows: TODO. If you can use the Linux subsystem and follow the Linux instructions.
• CppAD
  • Mac: brew install cppad
  • Linux sudo apt-get install cppad or equivalent.
  • Windows: TODO. If you can use the Linux subsystem and follow the Linux instructions.
• Eigen. This is already part of the repo so you shouldn't have to worry about it.
• Simulator. You can download these from the releases tab.
• Not a dependency but read the DATA.md for a description of the data sent back from the simulator.

Basic Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./mpc.

Reports

The Model

• The state is modele by a vector of x, y, psi, v, the first 2 terms (x, y) are the 2D coordinate of car in the coordinate space, and psi is the facing of the car to the angle of the x-axis and v is the absolute speed of car in the modeled state.
• Errors CTE(cross track error) and E-Psi(psi error) are the difference value the the reference trajectory(which is a fitted polynomial of degree 3).
• Actuators delta(steering angle) and a(throttle) are modeled as the angular acceleration and acceleration, but notice that the real angular acceleration should be double d_psi = v / Lf * delta * dt

Time step Length and Elapsed Duration (N & dt)

• T(The prediction horizon) equals to N(steps to predict) multiplies dt(time elapse per step)
• Although theoretically we want T to be as large as possible, but since we only fits the track by 6 points with a degree=3 polynomial and normally the environments change too much during time for our car to predict, it doesn't make sense to have a very large T.
• We normally want dt to be as small as possible, but the cumputational time increases as dt decreases and N increases. So after some trial and error, I ended up picking a T = 1.5 seconds where dt=0.08 (sec) and N=15 (steps).

The Results