Yuchen Lu

• Participated in the joint project “AI Applications Based on Ultrasound Imaging.”
• Conducted data preprocessing, including grayscale normalization, edge enhancement, and augmentation, to improve model robustness and output stability.
• Collaborated with other subgroups (Speech Recognition, Image Segmentation, ECG-AI) to align pipeline standards and data-sharing protocols across modules.
• Documented findings and presented progress at internal review sessions, focusing on visual interpretability and clinical usability of AI-generated images.

• Built a ROS2-based simulation pipeline for a mobile robot (unmanned vehicle) and a simple manipulator; organized packages, launch files, and configuration into a reproducible workspace for experimentation and demos.
• Implemented 2D SLAM to generate occupancy-grid maps and integrated localization into the navigation stack; practiced coordinate frame management with TF (e.g., map → odom → base_link) to ensure consistent perception–control alignment.
• Configured the Navigation2 stack for goal-directed navigation: set up a global planner (A*/Dijkstra) and a local, costmap-based avoidance planner; tuned inflation/obstacle layers to achieve collision-free path following in dynamic environments (simulation).
• Prototyped autonomous obstacle avoidance behaviors by combining sensor observations with local planner constraints, validating end-to-end navigation from map building → localization → planning → control in several test arenas.
• Explored computer vision fundamentals for robotics (image preprocessing, feature extraction, basic segmentation) and applied coordinate transformations between camera, robot, and target frames to support perception-guided motion.
• Collected run logs and scenario results to qualitatively evaluate navigation performance (e.g., success rate, path length, time-to-goal) and iterated parameters based on observed failure cases (corner traps, narrow passages).

Selected Tools: ROS2 (Navigation2), SLAM (2D occupancy mapping), TF, Python, C++ (basics), MATLAB (control & simulation), Linux, Git, RViz/Gazebo (simulation & visualization).

• Combining the knowledge learned at school, I have studied the practical use of languages such as Java, Python, and MySQL, and participated in tasks such as writing some simple software programs and organizing data; I am quite familiar with Java and Matlab, and have a basic understanding of Python.
• Adapted quickly to new technologies and programming languages as needed.
• Improved application performance with optimization of database queries, ensuring smooth and efficient data retrieval.
• Fostered culture of continuous learning and development within team, leading to increased innovation and problem-solving capabilities.