Zai Luo

Papers

Total Citations

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About

Zai Luo is a researcher specializing in robotics, autonomous navigation, and intelligent path planning, with a particular focus on measurement and multi-robot systems. Their work addresses critical challenges in robotic motion efficiency and environmental mapping. Luo’s most cited paper, “Path planning of a 6-DOF measuring robot with a direction guidance RRT method” (2023, 33 citations), introduces a novel Rapidly-exploring Random Tree (RRT) variant that significantly improves path stability and convergence in complex environments, a key contribution to automatic measurement. Another influential study, “Indoor Multi-Robot Cooperative Mapping Based on Geometric Features” (2021, 16 citations), proposes an innovative method for extracting overlapping regions between local maps using geometric features, enhancing both the efficiency and accuracy of collaborative mapping—a vital step for real-world multi-robot deployment. Luo’s latest work, “Path optimization of a flexible robot with a spatial compressed and a direction-guided exploring method” (2025), further advances the field by tackling multi-environment optimization challenges. With a growing citation record, Luo’s research is shaping the future of intelligent robotics, offering practical solutions for industrial automation and autonomous systems.