Automated lane following of a Waveshare JetRacer with artificial intelligence

Aus HSHL Mechatronik
Zur Navigation springen Zur Suche springen
Video 1: JetRacer mit Pytorch programmiert

Autor: Tasawar Siddiquy
Art: Bachelorarbeit
Dauer: 14.09.2022 -
Betreuer: Prof. Schneider


Aufgabenstellung

  1. Mechanischer und elektrischer Aufbau eines JetRacers
  2. Literaturrecherche zu den verfügbaren Programmierumgebungen
  3. Vergleich und Auswahl einer passenden Programmierumgebung
  4. Einarbeitung in die Programmierumgebung
  5. Anlernen des Deep Learning Netzwerkes zur Spurführung
  6. Modul und Systemtests mit dem JetRacer auf der Fahrbahn
  7. Ergebnisdarstellung und Ausblick
  8. Dokumentation des Umgang mit dem JetRacer im HSHL Wiki
  9. Bereitstellung von Demoprogrammen zum einfachen Einstieg
  10. Dokumentation nach wissenschaftlichem Stand

Anforderungen an die wissenschaftliche Arbeit

Getting Started

Nutzen Sie diese Artikel, um sich in das Thema einzuarbeiten:

Nützliche Artikel


Projektplan

Anforderungen

Hardwareanforderungen

Softwareanforderungen

Funktionaler Systementwurf / Technischer Systementwurf

Komponentenspezifikation

  • Specifications of the Jet-Racer

    Specifications of the Jet-Racer

Specifications of the Jet-Racer


Programmierung

For the implementation phase, a Jupyter notebook was utilized, and the code was developed in Python since it is extremely user-friendly and permitted direct wifi connectivity between the Windows PC and the Jetracer. A library file for the jetracer already exists. It has all of the basic functionalities for the jetracer and has been designed. First, using the Etcher tool, an SD card was produced with a prebuilt image based on the jetpack 4.5 file. This file was obtained from the waveshare website. Installing the jetpack 4.5 file followed. The NVIDIA JetPack software development kit is the most comprehensive instrument for creating apps with artificial intelligence. The JetPack software development kit is compatible with all Jetson modules and developer kits. The most recent Linux Driver Package for the Linux operating system is included in the JetPack software development kit, together with CUDA-X accelerated libraries and APIs for Deep Learning, Computer Vision, Accelerated Computing, and Multimedia. The software development kit further contains the Linux kernel. The micro SD card was then inserted into the Jetson nano, and the device is now set to connect to a wifi network using the Jupyter notebook. Having gotten the IP address via the USB connection, the system is now accessible from the personal computer. After successfully login in, the Jupyter notebooks' code will be available and can be modified. Following the installation of Python packages, the power mode should be specified. This may be seen on their website in full detail. First, it was evaluated using a controller that enables the user to manually manipulate the jetracer. The jetracer comes with this controller. Inside is a file for a simple motion and teleportation notebook, which the controller may use to operate the jetracer. It is advised that the controller be connected to the computer through the jupyter notebook, which then transfers the signal to the jetracer. The controller may be calibrated by utilizing the ipywidgets library. Using the Jupyter notebook, every aspect of the car, including the throttle gain, steering offset, and axes, may be modified.

The process of collecting data and feeding it into a neural network model is one of the most crucial components of developing one. For our needs, the data must portray the track from many perspectives. The jetracer's camera has been employed for this purpose. The camera widget is configured to show a preview on one screen and precisely define the direction vector, depending on which the autonomous vehicle will go, on the second screen. Use the widget's sliders to modify these parameters.

  • Block Diagram of the system

    Block Diagram of the system

Komponententest

Ergebnis

After successfully completing all of the processes, the car was able to drive without help. It has been put through a significant number of test laps, and each time it has demonstrated its ability to stay on the track and negotiate its lanes properly.

  • Driving Autonomously

    Driving Autonomously

Zusammenfassung

Lessons Learned

Projektunterlagen

SVN-Repositorium

YouTube Video

Das Video von diesem Projekt finden Sie auf Youtube unter dem Link:

Weblinks

Literatur

→ zurück zum Hauptartikel: Studentische Arbeiten

Abgerufen von „https://wiki.hshl.de/wiki/index.php?title=Automated_lane_following_of_a_Waveshare_JetRacer_with_artificial_intelligence&oldid=98756“