Mobile robots need to reliably perceive their sur- roundings, understand their position, and navigate safely to achieve useful autonomous behavior in real-world environments. This requires integrating sensors, perception algorithms, localization meth- ods, and control strategies into a coherent auton- omy stack that can run in real time on the robot. The Infineon Mobile Robot (IMR) provides a flexi- ble platform for developing and testing such auton- omy stacks. By adding sensors and implementing a perception and localization pipeline, the robot can move beyond basic teleoperation toward higher levels of autonomy. The use of a motion capture system, such as the Qualisys available at our insti- tute, further enables accurate ground-truth track- ing, which is essential for validating and bench- marking the performance of the autonomy stack under different conditions.
Currently, our IMR lacks a complete, modular au- tonomy stack integrating perception, localization, path planning, and control, along with Qualisys ground-truth evaluation capabilities. Our goal is to develop such a framework, enabling rapid ex- perimentation and performance benchmarking for future robotics research and student projects.
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Student Target Groups:
- Students of ICE/Telematics;
- Students of Computer Science;
- Students of Electrical and Digital Engineering.
Thesis Type:
- Bachelor Thesis / Master Project / Master Thesis
Goal and Tasks:
- Integrate sensors to the IMR platform and im- plement perception and localization algorithms, enabling the robot to understand its pose and environment reliably;
- Implement control and path planning algo- rithms, enabling navigation with basic obstacle avoidance and smooth motion execution;
- Integrate reflective markers on the robot for Qualisys motion capture compatibility, provid- ing accurate ground-truth position data for per- formance validation and benchmarking;
- Validate the autonomy stack through real-world experiments, comparing on-board location esti- mates against the ground truth obtained using the Qualisys, in order to ensure accuracy and robustness.
Recommended Prior Knowledge:
- Solid skills in Python and/or C++;
- Experience with robotics, ROS2, and Linux;
- Basic understanding of control theory, localiza- tion, perception, and planning algorithms.
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