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About

Qi Mao is a robotics researcher whose work spans legged locomotion, autonomous navigation, and agricultural automation. His research focuses on improving the stability and efficiency of robots operating in complex, unstructured environments—from rough terrain to agricultural fields. Mao’s most cited paper, “Research and Improvement on Active Compliance Control of Hydraulic Quadruped Robot” (2021, 20 citations), addresses the challenge of stable, high-load locomotion in hydraulic quadrupeds, a key area for military and industrial applications. He further explores structural optimization in “Natural Frequency Analysis of Hydraulic Quadruped Robot and Structural Optimization of the Leg” (2019, 6 citations), contributing to the theoretical foundation for efficient legged robot design. In mobile robotics, Mao’s “Efficient Path Planning Algorithm Based on Laser SLAM and an Optimized Visibility Graph for Robots” (2024, 11 citations) tackles the persistent challenge of real-time path planning in dynamic environments. His recent work extends to multi-robot systems and digital twins, with papers on distributed active information gathering for precision agriculture (2025) and a digital twin-driven sorting system for 3D printing farms (2025). These contributions demonstrate Mao’s growing impact in bridging robotics theory with practical, real-world applications in both industrial and agricultural settings.