Hefeng Lv

Papers

Total Citations

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About

Hefeng Lv is a researcher whose work centers on advancing autonomous navigation and motion planning for robotic systems. His primary contributions lie in developing efficient path planning algorithms, with a particular focus on improving the Rapidly-exploring Random Tree (RRT) method. Lv’s most cited paper, "Robot Path Planning Based on Improved RRT Algorithm" (2021), addresses critical challenges in real-time obstacle avoidance and computational efficiency, offering enhancements that reduce path length and convergence time. This work has garnered 3 citations, reflecting its relevance in the robotics community. Beyond this, Lv’s research explores the integration of heuristic strategies and adaptive sampling to make robot navigation more robust in complex environments. His efforts contribute to the broader goal of enabling autonomous systems to operate safely and effectively in dynamic settings, with potential applications in industrial automation, service robotics, and autonomous vehicles. Lv’s work is of particular interest to students and researchers seeking practical improvements in motion planning, as it bridges theoretical algorithm design with real-world implementation challenges.