Long Jin

Hong Kong Polytechnic University, Lanzhou University

Papers

Total Citations

543

H-Index

About

Long Jin is a prominent robotics and computational intelligence researcher whose work centers on distributed control systems, redundant robot manipulators, and neural network-based optimization. His research addresses some of the most challenging problems in multi-robot coordination, particularly how networks of robotic manipulators can collaborate effectively under real-world constraints such as limited communication bandwidth and environmental noise. Among his most influential contributions is his investigation into distributed task allocation for redundant robot manipulators, where he developed frameworks enabling selective deployment of the fittest robots within a group for precision path-tracking tasks — work that has garnered over 240 citations. His subsequent research on cooperative motion generation in distributed manipulator networks, cited nearly 180 times, extended these ideas by achieving global cooperation while simultaneously managing communication limitations and noise interference. Jin's work on neural computing is equally noteworthy. His development of the Varying-Parameter Convergent-Differential Neural Network (VP-CDNN) represents a significant advance in solving joint-angular-drift problems through quadratic programming formulations, accumulating over 120 citations. Collectively, his contributions bridge control theory, distributed computing, and intelligent robotics, making him a highly impactful figure for researchers working at these intersections.