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About

Yakun Ouyang is an emerging researcher specializing in autonomous robotics, trajectory planning, and multi-vehicle coordination, with a particular focus on solving complex optimization problems in constrained environments. His work addresses some of the most challenging aspects of autonomous navigation, including cooperative maneuver planning for multiple nonholonomic robots operating in cluttered, narrow spaces where conventional methods frequently fall short. Ouyang's most-cited contribution, "Optimal Cooperative Maneuver Planning for Multiple Nonholonomic Robots in a Tiny Environment via Adaptive-Scaling Constrained Optimization" (2021, 65 citations), introduced an adaptive-scaling framework that tackles the non-convexity and spatial constraints inherent to multi-vehicle trajectory planning — a problem notoriously resistant to efficient solutions. His subsequent work on "Embodied Footprints" (2023, 34 citations) advanced safety guarantees in optimization-based autonomous driving planners by addressing critical collision-avoidance gaps between trajectory collocation points. His benchmark study on fast and optimal multi-vehicle trajectory planning (2022, 25 citations) further solidified his reputation by providing rigorous methodological comparisons and experimental validation. With over 120 cumulative citations across just a few years, Ouyang's research is making a meaningful impact on the autonomous systems community, offering practical, mathematically rigorous solutions for real-world robotic deployment.