Papers
5
Total Citations
39
H-Index
5
About
Ming Lyu is a rising researcher at the forefront of autonomous robotics, specializing in multi-robot collaborative exploration, deep reinforcement learning (DRL), and motion planning. His work addresses critical challenges in unknown environments, such as search and rescue missions, where rapid and efficient mapping is essential. Lyu’s major contributions include pioneering a frontier-based robot exploration strategy enhanced by DRL, which has garnered 15 citations in 2024, and developing a transformer-based reinforcement learning framework for multi-robot systems, cited 7 times. He has also advanced path planning for robotic arms with an improved Informed RRT* algorithm, achieving 6 citations, and introduced a novel multi-robot exploration method that combines DRL with knowledge distillation to minimize redundant scanning in communication-constrained scenarios, earning 6 citations in 2025. Additionally, Lyu has contributed to quadruped locomotion with a real-time nonlinear model predictive control approach. With over 39 citations across his recent works, Ming Lyu’s innovative integration of learning-based methods with traditional robotics continues to push the boundaries of autonomous exploration and control.
Research Focus
Key Achievements
Top Papers
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