Tianyue Wu

Zhejiang University, State Key Laboratory of Industrial Control Technology

Papers

Total Citations

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About

Tianyue Wu is a rising researcher in robotics and autonomous systems, with a focus on enabling agile and safe navigation for aerial vehicles in complex environments. Their work bridges bio-inspired perception and computational geometry to address fundamental challenges in motion planning and control. Wu’s most-cited paper, “Learning Speed Adaptation for Flight in Clutter” (2024, 20 citations), introduces a novel framework that allows drones to dynamically adjust their flight speed based on environmental constraints and onboard capabilities—mimicking how animals trade off aggressiveness for safety. This work has been recognized for its potential to make autonomous flight more efficient and robust in cluttered settings. Additionally, Wu’s 2025 paper “Fast Iterative Region Inflation for Computing Large 2-D/3-D Convex Regions of Obstacle-Free Space” (10 citations) presents a computationally efficient method for generating high-quality convex polytopes that abstract obstacle-free spaces, addressing a critical bottleneck in real-time path planning. By combining learning-based adaptation with geometric optimization, Wu is contributing to the next generation of autonomous systems that can operate reliably in unpredictable, real-world environments. Their work is already influencing research in drone delivery, search-and-rescue, and exploration.