Haiming Li
Papers
2
Total Citations
31
H-Index
2
About
Haiming Li is a researcher focused on advancing autonomous navigation and motion planning for mobile robots, with particular expertise in path optimization and decision-making under uncertainty. His most impactful contribution is the development of Fast-RRT*, an improved motion planner for two-dimensional spaces that addresses key limitations of the asymptotically optimal rapidly-exploring random tree algorithm. This work, published in 2021 and garnering 29 citations, enhances the efficiency of collision-free path generation—a critical technology for unmanned systems. Li further explores the intersection of robotics and artificial intelligence in his 2022 work on robot navigation in crowded environments, where he applies deep reinforcement learning combined with Partially Observable Markov Decision Processes (POMDP) to enable robots to navigate safely among humans. While his citation counts reflect an emerging career, these contributions demonstrate Li’s commitment to solving real-world challenges in mobile robotics, from improving algorithmic efficiency to handling complex, dynamic environments. His research holds promise for applications in autonomous delivery, warehouse logistics, and service robotics.
Research Focus
Key Achievements
Top Papers
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