Jianliang Mao
Papers
9
Total Citations
201
H-Index
6
About
Jianliang Mao is a leading researcher in robotic control and autonomous manipulation, whose work centers on sensorless force estimation, real-time obstacle avoidance, and visual servoing for industrial and service robots. His most impactful contribution is a high-order finite-time observer scheme that enables precise interaction force estimation without physical force sensors, a breakthrough with 72 citations that reduces hardware costs while maintaining safety. Mao has also pioneered cascaded nonlinear MPC with artificial potential fields for dynamic obstacle avoidance (55 citations), allowing robot manipulators to react to moving obstacles in real time—a critical advance over traditional offline path planning. His recent work integrates control barrier functions with MPC to create flexible active safety motion control (20 citations), and he addresses dual-rate and view constraints in image-based visual servoing (12 citations). With over 200 total citations across his top papers, Mao’s research consistently bridges theory and practice, offering low-latency, high-precision solutions for complex environments. His achievements include developing a graph-optimized encoder-IMU fusion system for pipeline robot localization and a bridge-guided RRT* algorithm for navigating dense, narrow passages, underscoring his versatility in advancing both foundational control theory and applied robotics.
Research Focus
Key Achievements
Top Papers
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