Papers
2
Total Citations
94
H-Index
1
About
Xing An is a leading researcher in multi-robot systems and cooperative robotics, with a focus on enabling robust, scalable, and cost-effective autonomous operations. Their most cited work, "Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges" (2023, 93 citations), provides a comprehensive survey of the field, addressing critical challenges in coordination, communication, and platform design for real-world applications like cooperative object transport. This paper has become a foundational reference for researchers exploring decentralized multi-agent systems. More recently, An has advanced the intersection of reinforcement learning and robotics with their 2025 work on a federated reinforcement learning framework for mobile robot navigation using ROS and Gazebo. This innovative approach allows robots to learn adaptive navigation policies without pre-mapping, while preserving data privacy through federated learning—a significant step toward practical, scalable deployment. By bridging theoretical frameworks with simulation-based validation, An’s contributions are shaping the future of autonomous navigation and multi-robot collaboration, making them a key voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2