Shuli Hu

Papers

Total Citations

H-Index

About

Shuli Hu is a researcher in artificial intelligence and robotics, with a primary focus on path planning and navigation in dynamic environments. Their most notable contribution is the development of Jump Point Search with Temporal Obstacles, a groundbreaking algorithm that extends the classic Jump Point Search method to handle moving and time-dependent obstacles—such as those that appear, disappear, or shift over time. This work, published in 2021 and garnering 5 citations, addresses a critical challenge in 4-connected grid-based path planning, which is essential for applications ranging from video games to autonomous robotics. By enabling agents to navigate complex, temporally changing spaces efficiently, Hu’s research bridges the gap between theoretical search algorithms and real-world dynamic constraints. Their work has been recognized for its practical impact, offering a scalable solution to problems where static path planning falls short. Hu’s contributions are particularly valuable for students and researchers interested in robotics, game AI, and multi-agent systems, providing a foundation for further innovation in adaptive, real-time navigation.