moe-elsadig / carnd-p10

CarND Term 2 Model Predictive Control (MPC) Project

CarND-Controls-MPC

Self-Driving Car Engineer Nanodegree Program

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Please Check below for the dependancies

Video Sample

https://youtu.be/oJA1XanzMYs Note: at the end I speed up above the programmed limit all the way to the sharp turns and let go, notice how the MPC recovers quite elegantly in that situation ( proud moment:D )

The model.

If we consider the desired path as our "ground truth" with our goal to be that path, we can consider the control problem to be that of optimisation.

We have where we are now (x coor, y coor, v velocity, psi angle) and we know where we wish to be (e.g. the track's center) so it's a matter of a few equations to calculate our translational (cte crosstrack) error and our rotational/orientation (epsi) error. Doing so we can use our vehicle's mathematical representation (model) and predict the next number of steps into the future (N) at set increments of time (dt).

It's all about the timing!

Note that N has an effect on the computational complexity if set too high. dt should remain as low as possible but with the same consideration as N's increase, when using dt you should also be mindful of latency and incorporate as part of the model, an advantage of MPCs over PIDs.

At each step we predict N steps into the future, but, we only use the first prediction which is for the following (current) step only, the rest are discarded as the process is repeated from the new step. Storing the rest of the values can allow us to visualise the predicted trajectory at each step vs. the optimal path (green and yellow lines) in simulation to further study and optimise our cost functions.

Location, location, location?

Before starting off, our coordinates from the simulator are being fed to the MPC function as map coordinates, it is more efficient to preprocess these variables and change the perspective a little, like vehicle's for example! that's much more convenient. Doing so allows to simplify the state we pass to the MPC function and our CTE and EPSI calculations since px = py = psi = 0 now! convenient!.

We should also consider passing the path as a polynomial, and under the current circumstance (vehicle + roads) a 3rd order polynomial is quite sufficient at representing the curvature of most roads around (definitely in the simulator), and be sure to represent the order in the calculations for the acceleration (a) and steering angle (delta) in the MPC function when predicting.

Late.........ncy.......

Latency is a real issue, and a major advantage of the MPC. Modeling latency can simply be an addition to the vehicle model, where we are predicting for a certain time ahead of, not to mention incorporating punishments and pulling favourites when it comes to the cost calculation (if you dig through the code, the seemingly random numbers starting the cost calculations, that's how much I favour each one :p ), since we're running at a lag to the real world, it's easier to try and smooth things over, not take rash decisions since at any given moment, there is information we haven't caught up to yet and it's good to be careful and take things slow than jump the gun.

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.

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./