Papers
14
Total Citations
212
H-Index
9
About
Jianbin Xin is a robotics and autonomous systems researcher whose work spans multi-robot coordination, motion planning, and adaptive control — areas that sit at the intersection of artificial intelligence, optimization, and mechatronics. His most cited contributions include foundational work on Q-learning-based mobile robot path planning (41 citations) and a pioneering time-space network model for collision-free routing in multi-robot stations (40 citations), which redefined how cycle time minimization and collision avoidance are treated as unified problems in manufacturing environments. His parallel research on adaptive neural impedance control (40 citations) demonstrates equal fluency in intelligent control theory, leveraging neuro-adaptive observers to manage electrically driven robotic systems under uncertainty. More recently, Xin has advanced real-time multi-robot motion planning through distributed Model Predictive Contouring Control and LSTM-based trajectory planning, addressing critical bottlenecks in computational efficiency and adaptability. His energy-efficient routing framework and vision-based virtual impedance control further showcase his drive to bridge theoretical rigor with practical deployment. With over 200 cumulative citations and a rapidly growing portfolio, Xin represents an emerging voice shaping the future of intelligent, coordinated robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Path planning of a mobile robot in a free-space environment using Q-learning41 citations · 2018
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