Papers
2
Total Citations
9
H-Index
2
About
Xiaolong Li is a researcher specializing in robotics, path planning, and the application of artificial intelligence in sports technology. His work focuses on overcoming critical challenges in autonomous robot navigation, particularly through improvements to the artificial potential field method. In his highly cited 2017 paper, "Path Planning of Robot Based on Improved Artificial Potential Field Method," Li addresses two persistent issues in traditional algorithms: the "target unreachable" problem near obstacles and the tendency for robots to become trapped in local minima. By incorporating kinematic constraints, his approach enables more reliable and efficient navigation for mobile robots. More recently, Li has explored the intersection of deep learning and sports technology, as seen in his 2024 work on entertainment interactive robots for referee assistance in sports competitions. This research demonstrates his expanding interest in human-robot interaction and real-time decision support systems. With over 6 citations on his foundational path planning work alone, Li's contributions provide practical solutions for autonomous systems, making his research valuable for students and engineers working on robot control, AI-driven sports analytics, and intelligent automation.
Research Focus
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