Kunpeng Xu
Papers
3
Total Citations
12
H-Index
2
About
Kunpeng Xu is a researcher at the forefront of autonomous robotics and intelligent control systems, with a primary focus on deep reinforcement learning for path planning and trajectory tracking. His most impactful work introduces the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm as a robust solution for coverage path planning in unknown environments, enabling robots to efficiently cover designated areas with minimal redundancy—a critical challenge in autonomous exploration and inspection tasks. This work has garnered significant attention, accumulating 8 citations and establishing a foundation for further advances in the field. Xu has also made notable contributions to trajectory tracking by integrating Deep Deterministic Policy Gradient (DDPG) with Frenet coordinates, offering a novel approach to converting vehicle dynamics from Cartesian to Frenet frames for more precise control. His research bridges the gap between theoretical reinforcement learning and practical robotic applications, addressing real-world constraints such as unknown environments and dynamic obstacles. Through his innovative algorithms and rigorous experimental validation, Xu is shaping the next generation of autonomous navigation systems, making his work essential reading for students and researchers in robotics, control theory, and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3