Anton Lukyanenko

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About

Anton Lukyanenko is a researcher at the forefront of autonomous systems and multi-vehicle coordination, specializing in motion planning for complex, constrained environments. His work addresses the critical challenge of navigating non-holonomic vehicles—such as cars and robots—through crowded, non-Euclidean spaces where traditional decoupled planning fails. In his most-cited paper, "Probabilistic motion planning for non-Euclidean and multi-vehicle problems" (2023, 7 citations), Lukyanenko introduces a probabilistic framework that efficiently generates feasible trajectories in high-dimensional configuration spaces, enabling joint planning for multiple agents. His earlier work, "Optimal Localized Trajectory Planning of Multiple Non-holonomic Vehicles" (2021, 3 citations), tackles gridlocked intersections and tight parking areas, demonstrating a method that produces optimal, collision-free paths for multiple vehicles simultaneously. By integrating probabilistic sampling with optimal control, Lukyanenko’s contributions bridge the gap between theoretical motion planning and real-world deployment, offering scalable solutions for autonomous driving, warehouse logistics, and urban air mobility. His research is essential reading for engineers and students seeking to understand how to safely and efficiently coordinate multiple robots in shared, dynamic spaces.