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About

Ruslan Gayanov is a robotics researcher whose work focuses on the dynamic task of robotic catching—specifically, the real-time tracking and trajectory estimation of thrown objects. His core research lies at the intersection of computer vision, motion prediction, and evolutionary computation. Gayanov’s major contribution is a novel approach that applies genetic programming to forecast the future path of a moving object from video data captured by two cameras. This method allows a robotic system to anticipate where an object will be, enabling successful interception and catching. His most-cited paper, “Transportation of small objects by robotic throwing and catching” (2018, 9 citations), along with related works from 2017 and 2019, collectively establish a foundation for using biologically-inspired algorithms to solve complex, real-time control problems. By replacing traditional analytical models with an evolving program that learns trajectory patterns, Gayanov has opened a new pathway for more adaptive and robust robotic manipulation. His work is particularly notable for its practical application in logistics and automated material handling, where the ability to catch and transport objects on the fly could revolutionize efficiency.