Xiaoyang Gao
Papers
1
Total Citations
6
H-Index
1
About
Xiaoyang Gao is a researcher specializing in multi-robot systems, dynamic obstacle avoidance, and formation control, with a particular focus on applying artificial potential field methods to real-time robotic coordination. Their most cited work, "Multi-robot Rounding Strategy Based on Artificial Potential Field Method in Dynamic Environment" (2019, 6 citations), introduces an improved artificial potential field approach that enhances real-time online computing and local processing capabilities for multi-robot formation rounding in dynamic settings. This contribution addresses critical challenges in autonomous navigation, enabling robots to efficiently avoid obstacles while maintaining formation integrity. Gao’s research leverages the strengths of artificial field methods—strong real-time performance and fast local processing—to optimize multi-robot coordination, with implications for applications in search-and-rescue, warehouse automation, and autonomous driving. Their work demonstrates a practical balance between theoretical robustness and computational efficiency, offering a scalable solution for complex environments. While their citation count reflects a focused impact, Gao’s contributions are valuable for researchers exploring decentralized control strategies and real-time path planning in multi-agent systems, highlighting a commitment to advancing practical robotics solutions.
Research Focus
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Top Papers
- 1