Jingjing Jiang
Loughborough University, Imperial College London, Western University
Papers
7
Total Citations
247
H-Index
4
About
Jingjing Jiang is a leading researcher in intelligent robotic control and autonomous navigation, with a focus on developing robust, adaptive algorithms for robotic manipulators and mobile robots operating in uncertain and human-populated environments. Her major contributions span reinforcement learning-based control, shared control strategies, and socially-aware path planning. Notably, her 2021 work on reinforcement learning-based fixed-time trajectory tracking for uncertain robotic manipulators with input saturation, which has garnered 184 citations, introduces a novel actor-critic framework using radial basis function neural networks to guarantee rapid convergence and stability. Jiang has also pioneered shared-control algorithms for mobile robots, enabling safe teleoperation when absolute positioning is unavailable, and has advanced congestion-aware navigation by integrating spatial-temporal crowd anomaly detection to enhance long-term autonomy. Her recent work on continuous spatial-temporal routing further pushes the boundaries of socially-aware navigation, ensuring both efficiency and human comfort. With a career spanning foundational neuro-fuzzy control methods to cutting-edge Gaussian process-based adaptive sliding mode control, Jiang’s research is highly cited and instrumental for students and engineers seeking to deploy intelligent robots in dynamic, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2Shared Control for the Kinematic and Dynamic Models of a Mobile Robot34 citations · 2016
- 3Shared-control for the kinematic model of a mobile robot9 citations · 2014
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