You can browse the new JSBSim Online Reference Manual by going to. Starting from March 2018 a new effort is underway to deliver an up-to-date documentation web site. However, due to the nature of the development of the project (JSBSim sources are updated often, sometimes even daily), several new features that are available in the software are not yet documented in the reference manual. This link points to the official JSBSim Reference Manual, a PDF which is the best source of information for users and developers. Installing jsbsim using pip can be achieved with:Ī first place to look at for JSBSim documentation resources is. wheel packages are available from the Python Package Index (PyPI), a repository of software for the Python programming language. Installation with pipīinary packages a.k.a. These can be installed using either pip or conda. JSBSim provides binary wheel packages for its Python module on Windows, Mac OSX and Linux platforms for several Python versions (3.6, 3.7, 3.8, 3.9 and 3.10). python3-JSBSim_1.1. which installs the Python 3.6 module of JSBSim.JSBSim-devel_1.1. which installs the development resources (headers and libraries).JSBSim_1.1. which installs the executables JSBSim and aeromatic.Ubuntu Linuxĭebian packages for Ubuntu Linux "Bionic" 18.04 LTS and "Focal" 20.04 LTS for 64 bits platforms are also available in the JSBSim project release section. The Windows installer also contains the files needed to build the JSBSim Matlab S-Function (see our MATLAB README for more details about using JSBSim in Matlab). aeromatic.exe which builds aircraft definitions from Question/Answer interfaceīoth executables are console line commands.It installs the 2 executables along with aircraft data and some example scripts: User Guide Installation WindowsĪ Windows installer JSBSim-1.1.13-setup.exe is available in the release section. The work demonstrates an application of Deep Reinforcement Learning (DRL) to flight control and guidance, leveraging the JSBSim interface to MATLAB/Simulink. The open access article is available as a PDF here. In 2023 JSBSim has been featured in the article "A deep reinforcement learning control approach for high-performance aircraft" on Nonlinear Dynamics, an International Journal of Nonlinear Dynamics and Chaos in Engineering Systems by Springer. JSBSim is also used in academic and industry research ( more than 700 citations referenced by Google Scholar as of May 2023). DARPA Virtual Air Combat Competition where one of the AI went undefeated in five rounds of mock air combat against an Air Force fighter (see the video on YouTube).Machine Learning Aircraft control: gym-jsbsim.SITL (Software In The Loop) Drone Autopilot testing : ArduPilot, PX4 Autopilot, Paparazzi.Flight simulation: FlightGear, OutTerra, Skybolt Engine.Unreal Engine's Antoinette Project: tools to create the next generation of flight simulators. JSBSim is used in a range of projects among which: Most of the remaining differences are explained and could be reduced with further effort." Applications and Usages The results showed that the 7 simulation tools "were good enough to indicate agreement between a majority of simulation tools for all cases published. In 2015, NASA performed some verification check cases on 7 flight dynamics software including JSBSim (the other 6 being NASA in-house software).
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