Shuang Guo
Papers
1
Total Citations
10
H-Index
1
About
Shuang Guo is a robotics researcher whose work centers on multi-robot coordination, motion planning, and probabilistic inference. Their most notable contribution is the development of a centralized trajectory generation method for multi-robot formations, extending the influential GPMP2 framework. In their 2021 paper, Guo introduced a novel approach that uses a sparse Gaussian Process model to represent continuous-time trajectories for all robots simultaneously, enabling efficient and smooth formation control. This work has garnered 10 citations and addresses critical challenges in multi-robot systems, such as scalability and real-time feasibility. By leveraging probabilistic inference, Guo’s research bridges the gap between theoretical motion planning and practical deployment in complex environments. Their contributions are particularly valuable for applications in swarm robotics, autonomous navigation, and collaborative manipulation. Guo’s work stands out for its elegant mathematical formulation and potential to enhance coordination in dynamic, multi-agent settings, making them a rising figure in the field of robotic motion planning.
Research Focus
Key Achievements
Top Papers
- 1