Professor Açikmeşe received the IEEE 2023 Control Systems Magazine Outstanding Paper Award.

Behçet Açıkmeşe

A&A professor Behçet Açıkmeşe and A&A Ph.D. graduates Danylo Malyuta, Taylor Reynolds, and Michael Szmuk, together with colleagues from Stanford University, received the 2023 Control Systems Magazine Outstanding Paper Award for “Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently.”

The award recognizes outstanding papers in the IEEE Control Systems Magazine. This paper was selected for its originality, potential impact on theoretical foundations of control, importance and practical significance in applications and clarity.

Açıkmeşe describes the overall importance and significance of the research, “It presents a mathematically sound and computationally robust approach to solving trajectory optimization problems that are routinely encountered in dynamical systems, which include aerospace vehicles as well as ground and marine vehicles and robotic manipulators.”

He continues, “Since the proposed methods rely on efficient computational methods of convex optimization, they can be embedded on mobile computing platforms and executed autonomously in real time, making them highly applicable to many current and emerging autonomous vehicle applications.These techniques have already been utilized by many in aerospace industry for autonomous reusable rockets and spacecraft, as well as in autonomous ground vehicles.”

While there are many benefits to autonomous aerial vehicles delivering goods and medical supplies and space vehicles landing on planets, these applications present challenges with their stringent requirements on performance, safety and trustworthiness. Açıkmeşe’s paper introduces reliable and efficient convex optimization based trajectory generation methods and algorithms to meet given requirements while optimizing mission objectives, by using lossless convexification (LCvx), successive convex programming (SCvx), and guaranteed sequential trajectory optimization (GuSTO).