Yongping Ouyang
Papers
2
Total Citations
15
H-Index
2
About
Dr. Yongping Ouyang is a leading researcher in autonomous navigation and robotics, with a primary focus on mapless, collision-free navigation for mobile robots using deep reinforcement learning (DRL). His major contributions lie in developing innovative DRL-based frameworks that enable Unmanned Ground Vehicles (UGVs) and autonomous robots to navigate unstructured environments without relying on pre-mapped routes. Notably, his 2023 paper on "Deep Reinforcement Learning with Heuristic Corrections for UGV Navigation" (9 citations) introduces a novel approach that combines DRL with heuristic corrections to enhance collision avoidance from dynamic obstacles like pedestrians. Earlier, his 2019 work on "Mapless Navigation for Autonomous Robots" (6 citations) pioneered a decentralized collision avoidance method using spare observations, addressing the challenge of partial obstacle information. These contributions have garnered attention in the robotics community, with his papers cited for their practical impact on real-world navigation. Dr. Ouyang’s work is particularly valuable for students and researchers interested in the intersection of reinforcement learning and autonomous systems, offering robust solutions for safe, adaptive robot movement in complex environments.
Research Focus
Key Achievements
Top Papers
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- 2