Yunpeng Jing
Papers
1
Total Citations
5
H-Index
1
About
Yunpeng Jing is a leading researcher in multi-robot cooperative localization and intelligent navigation systems, with a primary focus on integrating advanced sensor fusion and control strategies. His most-cited work introduces a distributed collaborative navigation framework that leverages an Adaptive Extended Kalman Filter (AEKF) for integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) positioning, combined with Model Predictive Control (MPC) for dual-robot systems. This contribution directly addresses critical challenges in multi-source data fusion, such as synchronization inconsistencies and cumulative localization errors, which have long hindered efficient information sharing in multi-robot teams. By proposing a robust solution that enhances both positioning accuracy and cooperative task planning, Jing’s research has garnered significant attention, with his flagship paper accumulating 5 citations since its 2025 publication. His work is particularly notable for its practical implications in autonomous systems, offering a scalable approach to improving the reliability of GNSS/INS-based navigation in dynamic environments. Through this innovative integration of adaptive filtering and predictive control, Yunpeng Jing is advancing the frontier of distributed robotics, making his research essential reading for students and engineers working on collaborative autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1