Kefeng Guo

Papers

Total Citations

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About

Kefeng Guo is a leading researcher in autonomous robot navigation, with a primary focus on path planning and dynamic obstacle avoidance. His most cited work, "Hybrid Path Planning Algorithm Based on Improved Dynamic Window Approach" (2021, 6 citations), introduces a novel hybrid algorithm that advances beyond traditional static environment methods. By integrating an improved dynamic window approach, Guo's algorithm enables robots to effectively navigate complex, dynamic environments—a critical capability for real-world autonomous systems. This contribution addresses a key limitation in prior research, offering a more adaptive and robust solution for real-time path planning. Guo's work is particularly notable for its practical implications in robotics, where safe and efficient navigation among moving obstacles remains a significant challenge. His research bridges the gap between theoretical algorithms and real-world deployment, making him a respected figure in the field. With a growing citation record, Guo continues to influence the development of intelligent navigation systems, inspiring further innovation in autonomous robotics and human-robot interaction.