Kong Yingying
Papers
1
Total Citations
2
H-Index
1
About
Kong Yingying is a researcher in robotics and autonomous systems, with a primary focus on motion planning and algorithm development for intelligent machines. Her most notable contribution is the "Bidirectional Variable Probability RRT Algorithm for Robotic Path Planning" (2012), which introduced an innovative approach to rapidly-exploring random trees (RRT) by incorporating bidirectional search and variable probability sampling. This method significantly enhances the efficiency and success rate of path planning in complex, high-dimensional environments, addressing critical challenges in real-time robotic navigation. While her work has accumulated 2 citations, its conceptual foundation has informed subsequent advancements in sampling-based planning algorithms. Kong’s research sits at the intersection of probabilistic robotics and computational geometry, aiming to bridge the gap between theoretical algorithm design and practical deployment in autonomous vehicles, industrial manipulators, and service robots. Her contributions underscore the importance of adaptive, bidirectional strategies in overcoming the limitations of traditional RRT methods, making her a valuable voice in the ongoing evolution of robotic path planning.
Research Focus
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Top Papers
- 1