Danylo Malyuta

Papers

Total Citations

238

H-Index

About

Danylo Malyuta is a leading researcher in autonomous systems and aerospace engineering, whose work centers on the intersection of optimization, control theory, and trajectory generation. His major contributions lie in developing convex optimization-based methods that enable reliable, real-time trajectory planning for autonomous dynamical systems, from spacecraft to drones. Malyuta’s most influential work, the 2022 tutorial “Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently,” has amassed over 230 citations, establishing itself as a foundational resource in the field. In this comprehensive tutorial, he systematically explains three critical methods—lossless convexification (LCvx) and sequential convex programming—providing practitioners with the tools to generate dynamically feasible trajectories with guaranteed reliability. His research addresses a fundamental challenge: ensuring that autonomous vehicles can navigate complex environments safely and efficiently, even under tight computational constraints. Malyuta’s work has been instrumental in bridging theoretical optimization with practical deployment, making him a key figure in advancing the capabilities of next-generation autonomous systems.