/CarND-MPC-Project

CarND Term 2 Model Predictive Control (MPC) Project

Primary LanguageC++

CarND-Controls-MPC

Self-Driving Car Engineer Nanodegree Program

Introduction

This projects aims to provide my answer to the MPC project of Udacity SDCND. The MPC project includes two processing units -- the provided simulator (S) and the MPC algorithm (M). S sends out telemetry statistics (car position, velocity and heading) and some track waypoint data to M. After receiving the data from M and considering vehicle's motion model and vehicle's motion constraints, M send back actuator controls to S.

Rubric points

  • The Model: Student describes their model in detail. This includes the state, actuators and update equations.

The model state covers 6 elements: the car's x and y coordinates, the car's orientation angle (psi), the car's velocity, the cross-track error (cte) and psi error (epsi).

The model acturators cover 2 elements: the car's acceleration (a) and steering angle (delta).

The update equations are listed below:

equations

  • Timestep Length and Elapsed Duration (N & dt): Student discusses the reasoning behind the chosen N (timestep length) and dt (elapsed duration between timesteps) values. Additionally the student details the previous values tried.

The Udacity course suggested that N and dt can be 10 and 0.1, which means the MPC algorithm makes a decision of driving trajectory in 1 second duration (10 * 0.1). We also tried other pairs, for example, (50,0.1), (20,0.1) (10,0.5), (10,0.2) and (20,0.05). However, none of these performs better than (10,0.1).

  • Polynomial Fitting and MPC Preprocessing: A polynomial is fitted to waypoints. If the student preprocesses waypoints, the vehicle state, and/or actuators prior to the MPC procedure it is described.

The waypoints need to be preprocessed for easier polynomial fitting. This is implemented by transfroming the waypoints from map coordinate system to the car-centered coordinate system. Please check the main.cpp lines 105-110.

  • Model Predictive Control with Latency: The student implements Model Predictive Control that handles a 100 millisecond latency. Student provides details on how they deal with latency.

The 100-ms-long control latency means the actuations are taken into action 100 ms later. In other words, actuations at T will be applied at T+100ms. Thus, some changes are made to actuations assignments: instead of taking solution index 0 from MPC, I take index-i instead where matches the time after 0.1s. (please check the code MPC.cpp lines 103-108).

Simulation Video

A 2-min-long recorded video (record_video_480p.mov) of the simulation is released in youtube (https://youtu.be/tS3KRZFytHU) for evaluation. In my own laptop, the car runs safely for 20 minutes. Hope this video is illustrative.

Dependencies

  • cmake >= 3.5
  • All OSes: click here for installation instructions
  • make >= 4.1
  • gcc/g++ >= 5.4
  • 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.

Tips

  1. It's recommended to test the MPC on basic examples to see if your implementation behaves as desired. One possible example is the vehicle starting offset of a straight line (reference). If the MPC implementation is correct, after some number of timesteps (not too many) it should find and track the reference line.
  2. The lake_track_waypoints.csv file has the waypoints of the lake track. You could use this to fit polynomials and points and see of how well your model tracks curve. NOTE: This file might be not completely in sync with the simulator so your solution should NOT depend on it.
  3. For visualization this C++ matplotlib wrapper could be helpful.

Editor Settings

We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

  • indent using spaces
  • set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google's C++ style guide.

Project Instructions and Rubric

Note: regardless of the changes you make, your project must be buildable using cmake and make!

More information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project page for instructions and the project rubric.

Hints!

  • You don't have to follow this directory structure, but if you do, your work will span all of the .cpp files here. Keep an eye out for TODOs.

Call for IDE Profiles Pull Requests

Help your fellow students!

We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. Similarly, we omitted IDE profiles in order to we ensure that students don't feel pressured to use one IDE or another.

However! I'd love to help people get up and running with their IDEs of choice. If you've created a profile for an IDE that you think other students would appreciate, we'd love to have you add the requisite profile files and instructions to ide_profiles/. For example if you wanted to add a VS Code profile, you'd add:

  • /ide_profiles/vscode/.vscode
  • /ide_profiles/vscode/README.md

The README should explain what the profile does, how to take advantage of it, and how to install it.

Frankly, I've never been involved in a project with multiple IDE profiles before. I believe the best way to handle this would be to keep them out of the repo root to avoid clutter. My expectation is that most profiles will include instructions to copy files to a new location to get picked up by the IDE, but that's just a guess.

One last note here: regardless of the IDE used, every submitted project must still be compilable with cmake and make./