Controlled Autonomous Driving for a JetRacer

Abb. 1: JetRacer ROS AI Roboter von Waveshare

Autor:	Yashodhan Vishvesh Deshpande
Art:	bachelor thesis
Starttermin:	TBD
Abgabetermin:	TBD
Betreuer:	Prof. Dr.-Ing. Schneider

Task List

1. Research of similar projects
2. Evaluation of the solutions using a morphological box (Zwicky box)
3. Choose a software toolchain
4. Design and print a platform for the TopCon-prism.
5. Record the ground truth data
6. Track the JetRacer on a digital map.
7. Control the JetRacer in the right lane without obstacles by camera data.
8. Evaluate the results based on technical features
9. Discussion of the results
10. Testing of the system requirements - proof of functionality
11. Scientific documentation as a report
12. Scientific advertising as a wiki article with a result video.

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.

Links

• JetRacer ROS Kit
• Waveshare-Wiki: JetRacer ROS AI Kit

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