Xiaojing Li
Papers
1
Total Citations
2
H-Index
1
About
Xiaojing Li is a researcher in robotics and autonomous navigation, with a primary focus on indoor robot path planning and motion optimization. Her most notable contribution is the development of an improved probabilistic road map (PRM) method for indoor environments, which intelligently places nodes around obstacles to generate multiple feasible, collision-free routes for mobile robots. This work, published in 2019, addresses a critical challenge in real-world robotics: balancing computational efficiency with path safety in cluttered indoor spaces. By enabling robots to select from several alternative practical routes rather than a single optimal path, Li’s approach enhances adaptability in dynamic settings like warehouses, hospitals, and smart homes. While her citation count remains modest, the foundational nature of her work on probabilistic road map improvements has laid groundwork for subsequent studies in robot navigation. Li’s research is particularly relevant for students and engineers working on autonomous systems, as it bridges theoretical path planning algorithms with practical deployment constraints. Her contributions underscore the ongoing need for robust, real-time navigation solutions in increasingly automated indoor environments.
Research Focus
Key Achievements
Top Papers
- 1