Michael Szmuk

Amazon (United States), American Institute of Aeronautics and Astronautics

Papers

Total Citations

284

H-Index

About

Michael Szmuk is a leading researcher in the field of autonomous systems, specializing in trajectory generation and real-time motion planning. His primary research areas include convex optimization, lossless convexification (LCvx), and successive convexification techniques for enabling agile, safe, and computationally efficient autonomous flight. Szmuk’s major contributions center on transforming inherently non-convex trajectory planning problems into tractable convex formulations, allowing for reliable, on-board optimization in dynamic environments. His most cited work, the 2022 tutorial on convex optimization for trajectory generation (233 citations), has become a foundational resource for researchers and engineers working on autonomous dynamical systems. Notably, his 2017 paper on agile quad-rotor maneuvering and obstacle avoidance (46 citations) demonstrated the practical application of these methods for real-time, on-board computation, overcoming significant challenges in non-convex motion planning. Szmuk’s research has been instrumental in bridging the gap between theoretical optimization and real-world deployment, making him a key figure in the advancement of autonomous aerial vehicles and robotics.