Der JetRacer ist ein Modellbaufahrzeug im Maßstaß 1:10. Der Hauptsensor ist eine Monovideokamera und die Datenverarbeitung erfolgt auf einem NVIDIA® Jetson Nano™.
Aufgabenstellung
Einarbeitung in das bestehende Framwework und Dokumentation
Nutzung von MATLAB®/Simulink zum Anlernen des Jetson Nano.
Implementierung und Optimierung eines Reglers für die Spurführung (z. B. PD-Regler)
Optimierung der Spurführung anhand der Parameter Fahrgeschwindigkeit und Präzision der Spurführung
Bewertung der Vor- und Nachteile der Programmierungebungen
Dokumentation nach wissenschaftlichem Stand im HSHL-Wiki
Project Plan
The planning of the entire project
Worktime in the lab - Monday & Friday
Weekly Lab meeting - Monday
Research Results
Researching on the topic two most important part is Jetracer & Line following algorithm. And lots of research has been done on those regards topic and following are some notable references and which will be implemented on during this thesis duration.
Jetracer is a AI based race car made by NVIDIA [1] for various project such as line following , the full configuration, connectivity and other material of the Jetracer can also be found in the official GitHub Jetracer page [2].
For configuration and Connecting to the network following the link Jetracer: Teach-In Tutorial can be helpful to set up the full process.
Describing the training process and experiments related to autonomous movement [3], this paper can give an clear outline for the full process. Also for object and design development [4] this paper give full insight with CNN and more technique.
Line following following process is can be difficult in various way, for vision based technique shown in this paper [5] its also very challenging to achieve the goal. And for CNN based technique which also can be done [6] has been discussed in the paper.
Lastly for motion control of the Jetracer which will be operate real time the system can be control & manage is achieve in this paper [7].
Additional resources : For steering angle prediction [8], MATLAB deep learning model [9], Video classification in MATLAB [10].
As a Result while doing the research both of the method(Computer vision and CNN) can be done extensively and mostly vision based technique will be used in this process.
Choose Environment
For this thesis, MATLAB will be used as a main Application. It has several advantages while doing the process such as real time system [11], extensive MATLAB libraries and toolboxes, compatibility with Jetson nano hardware [12] .
For the setup of Jetracer hardware it can be easily done following the manual [13] and assemble it [14].
For Software setup firstly the Jetracer which has the Jetracer AI Kit on SD card to set it up and all the config. which the full process can be found in [15].
MATLAB also is been installed on the local device to operate and manage the Jetracer. Then the addon package of nvidia-jetson is also installed for the hardware package. Various other package also been installed for this project such as Deep-learning, computer-vision, also gpu-computing.
Model development
Firstly connecting to the Jetracer and MATLAB according to the [16] MATLAB documentation, which help up to setup the full process.
For modeling the project it can be make as simple as possible to understand the steps to follow, in this paper [17]it shows some of the ideas and implementation can be done. A outline of diagram can be: .
Component Requirements
Hardware Requirements
Software Requirements
Anforderungen
Das Projekt erfordert Vorwissen in den nachfolgenden Themengebieten. Sollten Sie die Anforderungen nicht erfüllen müssen Sie sich diese Kenntnisse anhand im Rahmen der Arbeit anhand von Literatur/Online-Kursen selbst aneignen.
Erfahrungen mit Künstlicher Intelligenz/Deep Learning