Xiaodong Peng
Papers
2
Total Citations
56
H-Index
2
About
Xiaodong Peng is a leading researcher at the intersection of robotics, unmanned aerial vehicles (UAVs), and multi-agent systems, with a particular focus on autonomous navigation and perception. His most impactful contribution is a pioneering approach to UAV autonomous tracking and landing using deep reinforcement learning, a work that has garnered 54 citations and demonstrates how machine learning can solve complex, real-time robotics tasks for both military and civil applications. More recently, Peng has tackled the critical challenge of multi-robot collaboration with his work on Semantic Map Registration under large perspective differences (SMR-GA). By employing a genetic algorithm to align sparse, outlier-ridden semantic maps from different viewpoints, this 2025 paper provides a robust solution for enabling teams of robots to build a cohesive understanding of their environment. This work is essential for advancing cooperative exploration and mapping. Peng’s research is notable for its practical, algorithm-driven approach to overcoming fundamental limitations in autonomous systems, making him a key figure in the development of more capable and collaborative robotic platforms.
Research Focus
Key Achievements
Top Papers
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