Papers
1
Total Citations
7
H-Index
1
About
Zhilong Xi is a researcher advancing the intersection of artificial intelligence and robotics, with a primary focus on autonomous navigation and obstacle avoidance. His most cited work, "Obstacle Avoidance Based on Deep Reinforcement Learning and Artificial Potential Field" (2023), introduces a novel hybrid algorithm that integrates deep reinforcement learning (DRL) with the classical artificial potential field method. By incorporating state regulation, Xi’s approach enhances the safety and efficiency of mobile robot path planning, addressing a critical challenge in automatic control. This paper, with 7 citations, demonstrates his ability to blend traditional control techniques with modern machine learning to solve real-world robotics problems. Xi’s contributions are particularly valuable for students and researchers exploring DRL-based navigation, as his work offers a practical framework for improving obstacle avoidance in dynamic environments. His research underscores the potential of hybrid methods to overcome the limitations of standalone approaches, marking him as a promising voice in the field of intelligent robotics and autonomous systems.
Research Focus
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Top Papers
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