Zeye Wu
Papers
1
Total Citations
5
H-Index
1
About
Zeye Wu is a researcher in robotics and computer vision, with a primary focus on vision-based navigation and obstacle avoidance for autonomous systems. His work centers on leveraging optical flow—the pattern of apparent motion in visual scenes—to enable robots to navigate dynamic environments safely and efficiently. In his most cited study, "An experimental evaluation of balance strategy based obstacle avoidance" (2016), Wu systematically assesses optical flow-driven methods for collision avoidance, comparing synthetic simulations with real-world performance. This research provides critical insights into the robustness of balance strategies, which mimic biological vision to maintain safe distances from obstacles. Though his citation count is modest, Wu’s contributions are foundational for researchers developing lightweight, vision-only navigation systems for drones and ground robots. His work bridges the gap between theoretical optical flow models and practical robotic applications, offering a benchmark for evaluating obstacle avoidance algorithms. For students and engineers entering the field, Wu’s research underscores the importance of experimental validation in translating biological principles into autonomous navigation solutions.
Research Focus
Key Achievements
Top Papers
- 1An experimental evaluation of balance strategy based obstacle avoidance5 citations · 2016