Papers
3
Total Citations
120
H-Index
3
About
Quoc Tran Dinh is a leading researcher in optimization-based robotics, whose work has fundamentally advanced the field of time-optimal path following. His primary research areas lie at the intersection of optimal control, convex optimization, and robotic motion planning, with a particular focus on enabling robots to move faster and more efficiently along predetermined paths. Dinh’s major contributions include the development of novel sequential convex programming frameworks that transform complex, non-convex time-optimal control problems into tractable convex formulations. His seminal 2013 paper, “Time-Optimal Path Following for Robots With Convex–Concave Constraints Using Sequential Convex Programming,” has garnered 98 citations, establishing a cornerstone methodology in the field. He further extended this work to address trajectory jerk constraints (18 citations) and Cartesian acceleration constraints, demonstrating the versatility of his convex optimization approach. By systematically handling actuator, jerk, and acceleration constraints, Dinh’s research has provided practical, computationally efficient solutions for high-speed robotic applications. His work is widely recognized for bridging the gap between theoretical optimal control and real-world robotic implementation, making him a key figure in modern motion planning and optimization.
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