Zhanlan Li
Papers
2
Total Citations
6
H-Index
2
About
Zhanlan Li is a rising researcher in the field of intelligent robotics, with a primary focus on deep reinforcement learning (DRL) for autonomous navigation and multi-agent coordination. Their work addresses critical challenges in robotic path planning, particularly for complex, obstacle-dense environments. Li’s most notable contribution is a novel DRL-based algorithm for robotic arm path planning, which introduces an innovative state representation technique that captures real-time environmental data with high fidelity. This foundational work, published in 2024, has already garnered 4 citations, signaling its early impact on the field. Building on this, Li extended their research to multi-robot systems, developing a queue formation and obstacle avoidance navigation strategy that enables collaborative task execution in hazardous or intricate settings. This 2025 publication, with 2 citations, demonstrates Li’s ability to tackle the growing need for scalable, cooperative robotic solutions. By integrating reinforcement learning with practical navigation constraints, Li is advancing the frontier of autonomous robotics, making significant strides toward systems that can operate safely and efficiently alongside humans. Their work is particularly relevant for students and researchers interested in the intersection of AI, control systems, and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2