Papers
8
Total Citations
159
H-Index
5
About
Qiuda Yu is a robotics researcher whose work bridges the gap between advanced control theory and practical locomotion for multi-legged and wheeled mobile robots. Their core research focuses on model predictive control, sliding mode control, and trajectory planning, with a particular emphasis on enabling robots to navigate complex, constrained environments. Yu’s most significant contributions include pioneering a trajectory tracking strategy for multi-legged robots that integrates model predictive and sliding mode control (41 citations), and developing a feasibility and control framework for a suctorial hexapod robot to transition between ground and wall surfaces (34 citations). They have also advanced formation control for multiple mecanum-wheeled mobile robots under physical constraints and uncertainties (31 citations), and proposed an omnidirectional tracking strategy for hexapod robots with rhythmic gaits that handles stride constraints through real-time replanning. Beyond locomotion, Yu has contributed to 6D object pose estimation using dense convolutional voting (5 citations) and energy-optimized trajectory deformation for robotic arms. Their work consistently addresses real-world challenges such as physical constraints, obstacle avoidance, and human-robot interaction, making their research highly relevant for students and engineers working on autonomous mobile robots and manipulation systems.
Research Focus
Key Achievements
Top Papers
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- 8Grasping Prediction Algorithm Based on Full Convolutional Neural Network2 citations · 2021