Xiao Fu
Papers
1
Total Citations
10
H-Index
1
About
Xiao Fu is a researcher specializing in autonomous underwater robotics, with a primary focus on motion planning and optimal search algorithms. Their most cited work, "Fast path planning for underwater robots by combining goal-biased Gaussian sampling with focused optimal search" (2021), introduces a novel hybrid approach that accelerates path planning in complex underwater environments—a critical challenge for autonomous vehicles operating in GPS-denied, obstacle-rich settings. By integrating goal-biased sampling with focused optimal search, Fu’s method significantly reduces computational overhead while maintaining trajectory quality, enabling faster and more reliable navigation for underwater robots. This contribution has garnered 10 citations, reflecting its relevance to the growing field of marine robotics. Fu’s work bridges the gap between sampling-based planners and optimal search techniques, offering practical solutions for real-world deployment in ocean exploration, environmental monitoring, and subsea infrastructure inspection. Their research underscores a commitment to advancing autonomous systems in challenging domains, making them a notable voice in the intersection of robotics and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1