Zhenyu Wu

Zhenyu Wu

Ph.D. student · mmWave radar sensing, robotic perception & state estimation

Radar-driven mobile robots

I initiated and helped build a mobile-robot testbed to connect mmWave radar sensing, state estimation and motion control. The platform includes raw-data acquisition robots, radar evaluation-module robots and complementary sensing configurations.

Five self-built heterogeneous mobile robots with different sensing configurations
The heterogeneous robot platforms used in our experiments.

My role

  • Initiated the project and coordinated a cross-group student team.
  • Worked on platform selection and assembly, radar/computing integration, and visualization and debugging tools.
  • Supported onboarding and weekly discussions to help students run experiments on the platforms.

Radar-based following

The following experiment uses relative radar measurements without an external positioning system. The clip below is extracted from the original research presentation.

Two-robot following experiment, recorded on the physical platform.

Multi-robot experiments

The recordings show formation initialization and motion on the physical platforms. These demonstrations are presented separately from the ongoing research on information-driven motion planning.

Formation initialization.
Multi-robot motion experiment.

Super-resolution point clouds

The comparison below comes from the point-cloud work presented at IEEE ISPCE-ASIA 2025. It compares conventional 2D-FFT, FFT + MUSIC and the proposed processing pipeline.

Point-cloud comparison: 2D-FFT, FFT with MUSIC, and the proposed method
Point-cloud comparison from the research presentation. Related paper: A Super-Resolution Point Cloud Generation Method for mmWave Radar.
Radar tracking visualization from the recorded experiment.

Related publication & software

  • F. Wang, C. Wang, P. Gao, Z. Wu, X. Yang, and Y. Shi. “A Super-Resolution Point Cloud Generation Method for mmWave Radar.” IEEE ISPCE-ASIA, 2025, pp. 1–6. DOI
  • ti_mobile_tracker_ros2: ROS 2 driver for TI Mobile Tracker point clouds and tracked targets.
  • Radar super-resolution research.

The platform supports projects CYB25139 and YBXM202502006.