Leijie Tang
Papers
1
Total Citations
13
H-Index
1
About
Leijie Tang is a robotics researcher whose work focuses on the locomotion and stability control of legged robots, particularly small-scale quadruped systems. His key research areas include model predictive control (MPC), gait planning, and real-time stability optimization for dynamic robotic movement. Tang’s most cited paper, “Trot Gait Stability Control of Small Quadruped Robot Based on MPC and ZMP Methods” (2023, 13 citations), addresses a critical challenge in quadruped robotics: maintaining stable trotting gait on uneven or rough terrain. By integrating MPC with the Zero Moment Point (ZMP) criterion, he developed a control framework that compensates for horizontal obstacles and vertical irregularities, significantly improving the robot’s ability to achieve desired gait patterns in real-world conditions. This work has been recognized for its practical contributions to enhancing the robustness and adaptability of small quadruped robots, making it a valuable reference for researchers in legged locomotion. Tang’s research bridges theoretical control methods with applied robotics, offering insights that advance the field toward more agile and reliable autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1