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cmake/third-party/CMakeLists.txt (588, 2023-08-16)
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cmake/third-party/lion.cmake (605, 2023-08-16)
database/ (0, 2023-08-16)
database/tracks/ (0, 2023-08-16)
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database/tracks/arezzo/Turn 1.png (173703, 2023-08-16)
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database/tracks/arezzo/Turn 5.png (166909, 2023-08-16)
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# Fastest-lap Fastest-lap is a vehicle dynamics simulator. It can be used to understand vehicle dynamics, to learn about driving techniques, to design car prototypes, or just for fun! [](https://github.com/juanmanzanero/fastest-lap/actions/workflows/macos.yml) [](https://github.com/juanmanzanero/fastest-lap/actions/workflows/linux.yml) [](https://github.com/juanmanzanero/fastest-lap/actions/workflows/windows.yml) [](https://fastest-lap.readthedocs.io/en/latest/?badge=latest) [](https://codecov.io/gh/juanmanzanero/fastest-lap) 

### What can be done - [Numerical G-G diagram][gg]: given a vehicle, and a speed, to compute its ax-ay diagram. The G-G diagram is a useful technique in vehicle design and parameters exploration. This is solved as an optimization problem: for a given lateral acceleration, find the minimum/maximum feasible longitudinal acceleration. - [Optimal laptime simulation][optimal-laptime]: given a vehicle, and a circuit, to compute the optimal controls that minimize the laptime. This problem is solved using a first order collocation method, the trapezoidal rule, with higher-order methods planned to be implemented soon. The NLP is solved using [Ipopt][ipopt], and [CppAD][cppad] to enhance its performance (a lap-time around Circuit de Catalunya can be obtained with 500 points in approximately 1 minute). This is not a quasi-steady-state simulation. The model solves the fully transient states as in the dynamic equations without steady-state assumptions. https://user-images.githubusercontent.com/26557659/163474269-5c195f4b-2109-419d-af49-7b7fa86a603d.mp4 [gg]: https://github.com/juanmanzanero/fastest-lap/tree/main/examples/python/kart/gg-diagram [optimal-laptime]: https://github.com/juanmanzanero/fastest-lap/tree/main/examples/python/f1/optimal-laptime ### The approach The core of the software is a C++ library, that can be used through a Python API. Full documentation is not yet available but some examples can be found in [examples/python][examples-python]. Fastest-lap is very efficient, being able to compute a full optimal lap in less than 1 minute. [examples-python]: https://github.com/juanmanzanero/fastest-lap/tree/main/examples/python ### Dynamic models The code implements two car models: - A [3DOF car model][link8] (longitudinal, lateral, and yaw), currently used for F1 simulations. The default parameters used can be found in [./database/limebeer-2014-f1.xml][database-f1] - A [6DOF car model][link2] (longitudinal, lateral, vertial, yaw, pitch and roll), currently used for Go-kart simulations. The default parameters used can be found in [./database/roberto-lot-2016-kart.xml][database] [database]: https://github.com/juanmanzanero/fastest-lap/blob/main/database/roberto-lot-kart-2016.xml [database-f1]: https://github.com/juanmanzanero/fastest-lap/blob/main/database/limebeer-2014-f1.xml ### Circuits Circuits are modeled from paths created from google earth, for example, the right track limit of Catalunya is included in this repository ([database/google_earth/Catalunya_right.kml][catalunya_right.kml]). Circuits are then preprocessed with a tool included herein to extract a reference line, its curvature, and the distance to the left/right track limits ([database/catalunya_discrete.xml][catalunya_discrete]). [catalunya_right.kml]: https://github.com/juanmanzanero/fastest-lap/blob/main/database/google_earth/Catalunya_right.kml [catalunya_discrete]: https://github.com/juanmanzanero/fastest-lap/blob/main/database/catalunya_discrete.xml ### Dependencies Fastest-lap uses several open-source libraries: - [Ipopt][ipopt]: Interior Point OPTimizer, is an open source software package for large-scale nonlinear optimization. Used within this project to obtain the solution to optimal laptime problems written as NLP (Non-linear programming problem). - [CppAD][cppad]: C++ Algorithmic Differentiation. Distributed alongside Ipopt, it is used to compute analytical derivatives. - [Tinyxml2][tinyxml2]: TinyXML-2 is a simple, small, efficient, C++ XML parser, used to read XML files (e.g. model parameters, tracks,...) - [logger-cpp][loggercpp]: a simple logger in C++, to handle print levels, and other interesting add-ons - [lion-cpp][lioncpp]: lightweigh interfaces for optimization and numerics, a C++ package manager for all the libraries mentioned above, plus other numerical methods such as mechanical frames, vector algebra, and Runge--Kutta schemes [ipopt]: https://github.com/coin-or/Ipopt [cppad]: https://github.com/coin-or/CppAD [tinyxml2]: https://github.com/leethomason/tinyxml2 [loggercpp]: https://github.com/juanmanzanero/logger-cpp [lioncpp]: https://github.com/juanmanzanero/lion-cpp ### Installation #### Windows 10 Precompiled binaries are available to download for every release. - [v0.1](https://github.com/juanmanzanero/fastest-lap/releases/tag/v0.1) - [v0.2](https://github.com/juanmanzanero/fastest-lap/releases/tag/v0.2) - [v0.3](https://github.com/juanmanzanero/fastest-lap/releases/tag/v0.3) - [v0.4](https://github.com/juanmanzanero/fastest-lap/releases/tag/v0.4) - [v0.5](https://github.com/juanmanzanero/fastest-lap/releases/tag/v0.5) Download and unzip. The contents of the zip folder are: - bin: the dynamic libraries. Fastest-lap C++ core is there. If fastest-lap is used from MATLAB, point `loadlibrary()` to this directory. - include: fastestlapc.h and fastest_lap.py. To use python scripts, make sure this folder is on the `PYTHONPATH` - examples: python notebook examples. - database: car and track data #### Mac and Linux This project uses CMake to build the source code and produce the binaries. The canonical steps to compile a CMake project are: (assume `$FASTESTLAP` is the source code top level.) 1. Create a build folder. ``` mkdir ${FASTESTLAP}/build ``` 2. From the build folder, run cmake ``` cd ${FASTESTLAP}/build && cmake .. ``` The options available for cmake are: ``` -DCMAKE_BUILD_TYPE=Debug/Release -DCMAKE_INSTALL_PREFIX=/path/to/install/dir -DCODE_COVERAGE=Yes/No: enables code coverage (if so, use with -DCMAKE_BUILD_TYPE=Debug) -DBUILD_DOC=Yes/No: builds doxygen documentation ``` At this stage, CMake will download and install all the thirdparty dependencies. 3. Compile ``` make ``` 4. Test (optional but recommended) ``` ctest --verbose ``` 5. Install (optional) ``` make install ``` #### Linux A Docker build environment is provided and can be used to compile the shared library and generate the Python bindings. ```shell sh ./src/scripts/linux/docker_compile.sh ``` ### Documentation Read the latest fastest-lap [online documentation](http://fastest-lap.readthedocs.io/) ### References [1] [Tremlett, A. J., and D. J. N. Limebeer. "Optimal tyre usage for a formula one car." Vehicle System Dynamics 54.10 (2016): 1448-1473.][link1]
[2] [Lot, Roberto, and Nicola Dal Bianco. "Lap time optimisation of a racing go-kart." Vehicle System Dynamics 54.2 (2016): 210-230.][link2]
[3] [Dal Bianco, Nicola, Roberto Lot, and Marco Gadola. "Minimum time optimal control simulation of a GP2 race car." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232.9 (2018): 1180-1195.][link3]
[4] [Lot, Roberto, and Matteo Massaro. "A symbolic approach to the multibody modeling of road vehicles." International Journal of Applied Mechanics 9.05 (2017): 1750068.][link4]
[5] [Kelly, Daniel P., and Robin S. Sharp. "Time-optimal control of the race car: a numerical method to emulate the ideal driver." Vehicle System Dynamics 48.12 (2010): 1461-1474.][link5]
[6] [Piccinini, Mattia. "Path planning and control of self-driving vehicles at the limits of handling"][link6]
[7] [Casanova, D. "On minimum time vehicle manoeuvring: the theoretical optimal lap"][link7]
[8] [Perantoni, G. et al. "Optimal Control for a Formula One Car with Variable Parameters"][link8]
[link1]: https://www.tandfonline.com/doi/abs/10.1080/00423114.2016.1213861 [link2]: https://www.tandfonline.com/doi/abs/10.1080/00423114.2015.1125514 [link3]: https://journals.sagepub.com/doi/pdf/10.1177/0954407017728158?casa_token=KJUTgUXmw7UAAAAA:rpL6chgRsgy6e8KagZ50jVeLOmITur5phRQYuh_PIY-WW7mMbEHSp-VCWvz3-wZ2FxkeeyhJR_t2 [link4]: https://www.worldscientific.com/doi/abs/10.1142/S1758825117500685 [link5]: https://www.tandfonline.com/doi/abs/10.1080/00423110903514236 [link6]: https://www.researchgate.net/publication/336880897_Path_Planning_and_Control_of_Self-Driving_Vehicles_at_the_Limits_of_Handling [link7]: https://dspace.lib.cranfield.ac.uk/handle/1826/1091 [link8]: https://web.archive.org/web/20200320055720id_/https://ora.ox.ac.uk/objects/uuid:ce1a7106-0a2c-41af-8449-41541220809f/download_file?safe_filename=Perantoni%2Band%2BLimebeer%252C%2BOptimal%2Bcontrol%2Bfor%2Ba%2BFormula%2BOne%2Bcar%2Bwith%2Bvariable%2Bparameters.pdf&file_format=application%2Fpdf&type_of_work=Journal+article

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