Duckietown

At LIS, we leverage the Duckietown hardware and software ecosystem to experiment with our reinforcement learning (RL) agents, known as learning classifier tables (LCTs), as part of the Duckiebot control system. More information on Duckietown can be found here.

In previous work, we developed a tool called DuckieVisualizer to monitor our Duckiebots, evaluate their driving performance, and visualize and interact with the actively learning RL agents.

This student assistant position will involve extending the tool and its respective interfaces on the robot side by further features, e.g., more complex learning algorithms or driving statistics. The underlying camera processing program should also be ported from Matlab to a faster programming language to enable real-time robot tracking. Furthermore, more robust Duckiebot identification mechanisms should be considered.

Besides these extensions to the DuckieVisualizer, the student will also do some general system maintenance tasks. This may include the hardware of the Duckiebots and their software stack, for example, merging different sub-projects and looking into quality-of-life improvements to the building process using Docker. Another task will be to help newly starting students set up their development environment and to assist them in their first steps. Finally, the student can get involved in expanding our track and adding new components, e.g., intersections or duckie pedestrian crossings.

Prerequisites

At LIS, we leverage the Duckietown hardware and software ecosystem to experiment with our reinforcement learning (RL) agents, known as learning classifier tables (LCTs), as part of the Duckiebot control system. More information on Duckietown can be found here.

We use a Duckiebot's Time-of-Flight (ToF) sensor to measure the distance to objects in front of the robot. This allows it to stop before crashing into obstacles. The distance measurement is also used in our platooning mechanism. When another Duckiebot is detected via its rear dot pattern, the robot can adjust its speed to follow the other Duckiebot at a given distance.

Unfortunately, the measurement region of the integrated ToF sensor is very narrow. It only detects objects reliably in a cone of about 5 degrees in front of the robot. Objects outside this cone, either too far to the side or too high/low, cannot reflect the emitted laser beam to the sensor's collector, leading to crashes. The distance measurement is also fairly noisy, with measurement accuracy decreasing for further distances, angular offsets from the sensor, and uneven reflection surfaces. This means that the distance to the other Duckiebot is often not measured correctly in the platooning mode, causing the robot to react with unexpected maneuvers and to lose track of the leading robot.

In this student assistant project, the student will investigate how to resolve these issues. After analyzing the current setup, different sensors and their position on the robot's front should be considered. A suitable driver and some hardware adaptations will be required to add a new sensor to the Duckiebot system. Finally, they will integrate the improved distance measurement setup in our Python/ROS-based autonomous driving pipeline, evaluate it in terms of measurement region and accuracy, and compare the new setup to the baseline.

These modifications should allow us to avoid crashes more reliably and enhance our platooning mode, which will be helpful for further development, especially when moving to more difficult-to-navigate environments, e.g., tracks with intersections and sharp turns.

Prerequisites