Jiuyang Gao
Papers
1
Total Citations
6
H-Index
1
About
Jiuyang Gao is a researcher whose work focuses on advancing robotic path planning, particularly through improvements to sampling-based algorithms for complex environments. His most-cited contribution, "APF-IBRRT*: A Global Path Planning Algorithm for Obstacle Avoidance Robots With Improved Iterative Search Efficiency" (2024), tackles a critical limitation of the Rapidly-exploring Random Tree (RRT) family. While RRT algorithms are valued for their collision-free, asymptotically optimal solutions, they struggle in narrow and dynamic indoor spaces. Gao’s APF-IBRRT* integrates artificial potential fields with an improved bidirectional RRT* to enhance search efficiency and obstacle avoidance in such challenging settings. With 6 citations already, this work demonstrates early impact in the robotics community. His research addresses the practical need for faster, more reliable navigation in real-world environments, from service robots to autonomous vehicles. By refining the balance between exploration speed and path optimality, Gao contributes to making robotic systems more adaptive and robust. His work is particularly relevant for students and engineers developing autonomous navigation systems that must operate safely in cluttered, unpredictable spaces.
Research Focus
Key Achievements
Top Papers
- 1