ZhenNi Liu
Papers
1
Total Citations
5
H-Index
1
About
ZhenNi Liu is a researcher in robotics and autonomous systems, with a focus on adaptive obstacle avoidance and intelligent decision-making algorithms. Their most cited work, "Research on Adaptive Obstacle Avoidance Algorithm of Robot Based on Ddpg-Dwa" (2023), integrates deep reinforcement learning (DDPG) with the Dynamic Window Approach (DWA) to enhance robot navigation in dynamic environments. This contribution addresses a critical challenge in mobile robotics—balancing real-time path planning with collision avoidance—by enabling robots to learn and adapt their movement strategies through trial and error. With 5 citations, the paper has already garnered attention for its practical implications in autonomous vehicles and service robots. Liu’s research bridges the gap between theoretical reinforcement learning and real-world robotic applications, offering a scalable solution for complex, unpredictable settings. Their work underscores a commitment to advancing safe, efficient autonomous navigation, making it a valuable reference for students and researchers exploring adaptive control systems.
Research Focus
Key Achievements
Top Papers
- 1