Jingqiao Liu
Papers
1
Total Citations
18
H-Index
1
About
Jingqiao Liu has made significant contributions to the field of mobile robotics, with a primary focus on real-time dynamic path planning and obstacle avoidance. Their most-cited work, a 2021 study combining artificial potential field methods with biased target RRT algorithms, has garnered 18 citations for its innovative approach to improving navigation efficiency in complex, dynamic environments. This research addresses a critical challenge in autonomous systems: enabling robots to adaptively plan safe, efficient paths while avoiding moving obstacles in real time. Liu’s work bridges theoretical algorithm design with practical implementation, offering a robust solution that enhances the responsiveness and reliability of mobile robots. By integrating complementary path planning techniques, they have advanced the state of the art in autonomous navigation, with implications for applications ranging from warehouse logistics to search-and-rescue operations. Their contributions underscore a commitment to solving real-world robotics problems, making their research a valuable resource for students and engineers seeking to develop more intelligent and adaptive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1