Controlled Autonomous Driving for a JetRacer: Unterschied zwischen den Versionen
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= Theoretical Background = | = Theoretical Background = | ||
''' Hardware''' | ''' Hardware Requirements''' | ||
The Jetracer ROS Ai kit is used for this thesis as a primary autonomous vehicle. All its system that | :The Jetracer ROS Ai kit is used for this thesis as a primary autonomous vehicle. All its system that | ||
are integrated in the Jetracer were used for example the LIDAR, Gyroscope, camera, motors, Jetson | are integrated in the Jetracer were used for example the LIDAR, Gyroscope, camera, motors, Jetson | ||
nano board, Raspberry Pi RP2040 board and the batteries. The autonomous system Laboratory | nano board, Raspberry Pi RP2040 board and the batteries. The autonomous system Laboratory | ||
Version vom 29. Juni 2026, 22:21 Uhr
| Autor: | Yashodhan Vishvesh Deshpande |
| Art: | bachelor thesis |
| Starttermin: | 20.01.2026 |
| Abgabetermin: | 19.05.2026 |
| Betreuer: | Prof. Dr.-Ing. Schneider |
Task List
- Research of similar projects
- Evaluation of the solutions using a morphological box (Zwicky box)
- Choose a software toolchain
- Design and print a platform for the TopCon-prism.
- Record the ground truth data
- Track the JetRacer on a digital map.
- Control the JetRacer in the right lane without obstacles by camera data.
- Evaluate the results based on technical features
- Discussion of the results
- Testing of the system requirements - proof of functionality
- Scientific documentation as a report
- Scientific advertising as a wiki article with a result video.
Thematic Introduction
The JetRacer:_ROS_AI_Kit can be programmed in different ways. The mechatronic student learn to develop C-code, m-code oder model based development with simulink. The task is to develop a "easy to use" framework to get quick results. After choosing the toolchain based on a morphological box an attachment for the reference prism (Referenzmessung_mit_der_Topcon_Robotic_Total_Station) has to be build. By this the ground-truth-data (vehicle pose) can be recorded for further analyzing. The main task is to develop a program that acquires an image frame, transforms it into bird-eye-view to measure the lane's curvature. Based on the lane curvature and actual driving status the car should steer into the right lane. Optional a gyroscope can measure the yaw-rate of the JetRacer.
The goal is to drive in the right lane as fast as possible.
Optional an obstacle detection an avoidance by LiDAR can be added. Future students can compare an AI lanekeeping to this classic image processing approach.
The Bachelor Thesis was carried out in the Autonomous Systems Laboratory at Hochschule Hamm-Lippstadt. The Bachelor Thesis was carried out under the supervision of Prof. Dr. Ulrich Schneider and Prof. Marc Ebmeyer. The Laboratory is designed for Mechatronic and Electronic Engineering Students to realize there Academic Projects.
Bachelor Thesis Plan
The image shown in Fig.2 is the thesis timeline that is illustrated using a Gantt Chart.
Theoretical Background
Hardware Requirements
- The Jetracer ROS Ai kit is used for this thesis as a primary autonomous vehicle. All its system that
are integrated in the Jetracer were used for example the LIDAR, Gyroscope, camera, motors, Jetson nano board, Raspberry Pi RP2040 board and the batteries. The autonomous system Laboratory PC was used for the image processing. The operating system for the Jetracer was installed on a external 64 GB micro SD card. To setup the OS a USB supported mouse and keyboard along with HDMI supported monitor was used. The autonomous system laboratory Wifi was used for communication between the Jetracer and the image processing PC. The Topcon Total robotic station PS-103A was used to measure the ground truth of the vehicle by prism tracking. The 3D printing was performed using laboratory facilities of the university. A checkerboard black and white square pattern of 30.83mm length was used to calibrate the camera. Lastly a infrared sensor based speed measuring device was used to measure the real time speed of the vehicle.
| Component Type | Component Name |
|---|---|
| Autonomous Vehicle | Jetracer ROS AI Kit |
| Image processing Unit | Autonomous System Laboratory Windows 10 PC |
| OS platform | 64 GB mircro SD card |
| Communication setup | Autonomous System Laboratory Wifi |
| Jetracer OS setup hardware | USB supported keyboard, mouse and HDMI supported monitor |
| Ground truth data tracking | TopCon Robotic Total Station PS-103A Kit |
| 3D printing | HSHL University 3D printer |
| Camera calibration setup | Autonomous system laboratory camera calibration checkerboard |
| Vehicle speed measurement | Infrared based vehicle speed measurement device |
Solutions
Results
Summary and Outlook
Repository
- Please link your source code here.
- Source Code should match the coding guidelines (MATLAB® | C)
- Add a header and help to your files to make them understandable for the team (MATLAB® | C).
- Describe what source code does what.
- What are the preconditions in hardware and software (Autonomous Driving Toolbox, Parallel Computing Toolbox, etc.)?
- Wird eine spezielle Hardware/GPU benötigt?
List of open questions
- The video has images sized 360px x 480px. This does not fit he calibration parameters cameraParams.Intrinsics.ImageSize (480 x 640). This has to match.
Links
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