Zhiwei Hou
Papers
2
Total Citations
20
H-Index
2
About
Dr. Zhiwei Hou is a robotics researcher specializing in autonomous navigation and collision avoidance for mobile and aerial robots. His work centers on applying deep reinforcement learning (DRL) to enable mapless, sensor-driven path planning—a critical challenge in unstructured environments. Hou’s most cited paper, “DRL-based Path Planner and its Application in Real Quadrotor with LIDAR” (2023, 17 citations), demonstrates a novel framework that translates raw LIDAR data directly into control commands for quadrotors, achieving real-world deployment. His earlier work, “Automatic Collision Avoidance via Deep Reinforcement Learning for Mobile Robot” (2022), proposed a mapless algorithm that maps sensor inputs to optimal trajectories, addressing a fundamental problem in mobile robotics. Though early in his career, Hou’s contributions are notable for bridging simulation-to-reality gaps, with his quadrotor study serving as a rare example of DRL-based navigation validated on physical hardware. His research holds promise for applications in search-and-rescue, warehouse automation, and drone delivery, where robust, reactive navigation is essential.
Research Focus
Key Achievements
Top Papers
- 1DRL-based Path Planner and its Application in Real Quadrotor with LIDAR17 citations · 2023
- 2