Härtill 3 uppsatser

Lic. (sammanfattning) Göteborg : Chalmers tekniska högskola, 2024

With the rapid emergence of autonomous driving technology, safety tests have become crucial for validating the functionality of these systems. High-fidelity scenarios of the tests typically involve various types of road users. To ensure safety during tests, human drivers are excluded, and instead, dummies and robots are used to simulate real road users. The test objects should follow pre-defined trajectories timely, and interact with each other. Then the on board ADAS systems’ reaction may be tested in a controllable and repeatable manner. In this thesis, we developed an autonomous robot bicycle to serve in the aforementioned safety tests. To build the robot, an electric bicycle has been modified with sensors and actuators, and the target is to follow a specified trajectory with a dummy cyclist mounted on the saddle. Several modules are essential for this purpose. Firstly, a state estimator is designed to compute the bicycle’s states using sensor fusion techniques. Secondly, a speed-dependent controller is developed through system identification technique, which estimates the dynamics of the unstable bicycle based on experimental data. Finally, an Iterative Learning Controller (ILC) is designed to exploit the repetitive nature of safety tests, enhancing trajectory tracking performance through iteration. These modules are detailed in the three papers included in this thesis.