Quanjie Gao
Papers
1
Total Citations
14
H-Index
1
About
Quanjie Gao is a leading researcher in robotics and intelligent control systems, with a primary focus on multiobjective trajectory optimization and human-robot interaction in uncertain environments. His most notable contribution is the development of the RBF–NSGA-II framework, a novel approach that integrates radial basis function networks with a multiobjective genetic algorithm to solve complex trajectory planning problems for wall-building robots operating in viscoelastic contact environments. This work, published in 2023 and already garnering 14 citations, addresses critical challenges in construction robotics by simultaneously optimizing energy consumption, contact forces, work efficiency, and motion smoothness. Gao's research has significant implications for the automation of construction tasks, where robots must navigate uncertain and deformable surfaces while maintaining precision and safety. His segmented multiobjective trajectory optimization methodology represents a breakthrough in enabling robots to adapt to real-world, non-ideal conditions. By tackling the trade-offs between competing performance metrics, Gao has advanced the field of robotic manipulation in unstructured environments, making his work essential reading for researchers in construction automation, soft robotics, and intelligent control systems.
Research Focus
Key Achievements
Top Papers
- 1