Qingni Yuan
Papers
8
Total Citations
248
H-Index
7
About
Qingni Yuan is a robotics and autonomous systems researcher whose work centers on motion planning, path optimization, and robotic manipulation. With a body of work that has accumulated over 248 citations, Yuan has established a strong reputation for developing innovative algorithmic solutions to some of the most persistent challenges in robot navigation and arm control. Yuan's most impactful contributions lie in advancing sampling-based planning algorithms, particularly variants of the Rapidly-exploring Random Tree (RRT*) family. Their 2022 paper on an improved P_RRT* algorithm (66 citations) and a 2023 follow-up integrating BP neural networks (61 citations) directly tackle the critical bottlenecks of convergence speed and computational efficiency in six-degree-of-freedom manipulator planning within complex, obstacle-rich environments. Complementing this work, Yuan has made notable strides in swarm intelligence methods, applying differential evolution-enhanced Particle Swarm Optimization to mobile robot navigation, and in hybrid approaches combining Artificial Potential Fields with RRT frameworks. More recently, Yuan's research has expanded toward perception-driven robotics, with a 2025 study leveraging vision-language models for optimal grasp pose detection — signaling a compelling evolution toward semantically aware robotic systems. Yuan's work represents a rigorous and practical contribution to making robots faster, safer, and more intelligent.
Research Focus
Key Achievements
Top Papers
- 1Path planning of a manipulator based on an improved P_RRT* algorithm66 citations · 2022
- 2
- 3
- 4
- 5
- 6
- 7
- 8Detection of Robot Optimal Grasping Pose Based on Vision-Language Models1 citations · 2025