Xiaoqing Wang
Papers
2
Total Citations
12
H-Index
2
About
Xiaoqing Wang is a leading researcher in mobile robotics and autonomous navigation, with a primary focus on path planning algorithms for critical applications. Their work addresses the fundamental challenge of enabling robots to navigate complex, high-dimensional environments efficiently and safely. Wang’s major contributions include developing an improved Iterated Greedy Algorithm for rescue robot path planning, which optimizes routes under the constraint of limited survival time—a variant of the traveling salesman problem (TSP) with 10 citations. They have also advanced sampling-based methods with an efficient RRT*-based path planner that achieves a fast convergence rate, ensuring near-optimal solutions in nonlinear environments. Wang’s research has direct implications for post-disaster rescue operations, where rapid and reliable navigation can save lives. With a growing citation impact, their work bridges theoretical algorithm design and practical deployment, making notable strides in improving robot autonomy. Wang’s achievements highlight a commitment to solving real-world problems through innovative computational approaches, positioning them as a key contributor to the future of intelligent mobile systems.
Research Focus
Key Achievements
Top Papers
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- 2