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About

Igor Prokopiev is a robotics researcher whose work focuses on the autonomous navigation and real-time control of mobile robots, particularly car-like and unmanned vehicles operating in complex, constrained environments. His major contributions lie in two interconnected areas: developing advanced trajectory planning methods and creating robust navigation systems that do not rely on external positioning. Prokopiev introduced the use of the network operator method for cost function synthesis in trajectory planning, a state-space sampling strategy that proves more effective than traditional control-space sampling when environmental constraints are severe. He has also pioneered the use of identified neural network models for autonomous navigation, enabling robots to accurately determine their position and navigate without GPS or external beacons. His research further extends to nonlinear model identification using neural network autoregressive models, validated in simulators like Gazebo. While his citation counts (2–4 per paper) reflect a developing career, his work on integrating neural identification with control synthesis for real-time applications represents a significant step toward more autonomous and adaptable robotic systems.