Papers
4
Total Citations
49
H-Index
3
About
Vassilios Yfantis is a robotics and automation researcher whose work sits at the intersection of optimal control, scheduling theory, and intelligent systems for industrial applications. His research primarily focuses on coordinating multiple robotic manipulators in shared workspaces — a challenge that demands simultaneous solutions to collision avoidance, trajectory planning, and task scheduling. His most influential contribution, "Optimal Scheduling and Model Predictive Control for Trajectory Planning of Cooperative Robot Manipulators" (2020, 33 citations), introduced a hierarchical control framework enabling two robot arms to perform collision-free pick-and-place operations with moving objects, establishing a meaningful benchmark in cooperative manipulation. Building on this foundation, Yfantis extended his work to non-cooperative distributed model predictive control for multi-robot systems and dynamic real-time path planning, pushing manipulator deployment beyond repetitive, structured tasks into flexible production environments. More recently, he has embraced reinforcement learning as a tool for solving complex job-shop scheduling problems, reducing reliance on hand-crafted heuristics. Collectively, his research addresses a critical gap in modern manufacturing: enabling intelligent, adaptable robotic systems capable of operating safely and efficiently in dynamic, human-shared workspaces.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4