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Termin |
Planung für die Woche |
Fortschritt
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1 |
12.06.2024- 19.06.2024 |
- Safety briefing for Marc Ebmeyer's laboratories
- Familiarization with the existing frame work
- Familiarize with the existing system using the wiki article. Gantt chart
- Handing over the keys to Marc Ebmeyer's laboratories
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- Make Research on the topic
- Draft a timeline for the project.
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2 |
20.06.2024-26.06.2024 |
- Build the Jetracer
- Setup and Flash Nvidia OS
- Connect JetRacer to local WLAN
- Setup Headless control of jetracer
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- Errors and Crashes on the OS (details on Errors and solutions)
- Find the optimal OS for the jet racer
- Optimization of the AI for the circuit, the controller (e.g. PD controller) in the laboratory Autonomous systems (speed, robustness).
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3 |
26.06.2024 - 05.07.2024 |
- Fixing, debuging errors and testing different Flashversions of the JetRacer
- Teach the JetRacer automated driving
- Steering JetRacer with Gamepad
- Teaching Jetracer interactive_regression
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- Make Research on Matlab, Deep learning and ROS
- Document Errors
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4 |
05.07.2024 - 26.07.2024 |
- Link Jetracer to MATLAB
- Install prerequisite for the jetracer like: CUDA, Cudnn, TensorRT
- programme Jetracer using MATLAB:
- Image recognition
- Line following
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- Document Errors
- Speak with other student working on Autonome Spurführung mit einem JetRacer ROS AI Robot
- Update the Professor by Email for Feedback
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6 |
29.07.2024 - |
- Programme Jetracer using ROS2
- Evaluation of the advantages and disadvantages of the programming environments.
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Dokumentieren Sie bitte hier Ihren Fortschritt.
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7 |
12.08.2024 |
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8 |
19.08.2024 |
- Use the Game controller to steer
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Dokumentieren Sie bitte hier Ihren Fortschritt.
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9 |
26.08.2024 |
- Programme Jetracer using ROS2
- Evaluation of the advantages and disadvantages of the programming environments.
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Dokumentieren Sie bitte hier Ihren Fortschritt.
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10 |
04.09.2024 |
- Discussion of subject for the bachelorthesis.
- Drive the JetRacer in the right lane counterclockwise with the gamepad controller. Limit the speed via Software to a maximum (e. g. 1 m/s).
- Take a video while driving a lap with MATLAB® using a MATLAB®-script.
- Load the pretrained NN.
- Train the pretrained NN with MATLAB® with a MATLAB®-App (GUI) by clicking the desired path in the images.
- Use classic lane tracking algorithms to teach the NN automatically.
- Write a PD-contoller that uses the NN to drive in the right lane. Program this in MATLAB® and let it run on the JetRacer-GPU using GPU Coder.
- Goal: the car should drive autonomously several laps in the right lane as fast as possible.
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Dokumentieren Sie bitte hier Ihren Fortschritt.
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11 |
09.09.2024 |
- Student did not attend the meeting.
- Please cancle the meeting if you are not coming.
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Dokumentieren Sie bitte hier Ihren Fortschritt.
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