Real-Time Optimization for Autonomous Control
Tuesday, October 30, 3:30PM – 5PM
Many future engineering applications will require dramatic increases in our existing Au- tonomous GN&C (Guidance, Navigation and Control) capabilities. These include robotic sample return missions to planets, comets, and asteroids, formation flying space- craft applications, applications utilizing swarms of autonomous agents, unmanned aerial, ground, and underwater vehicles, and autonomous commercial robotic applications. The main GN&C challenge for many autonomous systems is to achieve the performance goals safely with minimal resource use in the presence of mission constraints and uncertainties. A key difficulty in meeting this challenge is the ability to solve these complex GN&C decision making problems autonomously onboard.
The majority of the advanced autonomous GN&C problems are constrained optimization problems. However, optimization has traditionally been regarded as unsuitable for on- board autonomous use in space applications, mainly because there are hard requirements to guarantee obtaining a solution autonomously in real-time and with limited onboard processing. While satisfying these requirements for general optimization problems may not be possible, the Convex Optimization problems can be solved quickly to global op- timality in a predetermined number of computations, that is, we can guarantee finding an optimal solution without a human in the loop. This makes convex optimization a powerful tool to meet the onboard autonomous GN&C challenges.
Our research has provided new analytical results that enabled the formulation of many autonomous GN&C problems in a convex optimization framework. This presentation in- troduces several real-world examples where this approach either produced dramatically improved performance over the heritage technology or enabled a new technology. The examples include autonomous exploration of comets and asteroids, formation flying, and swarms of autonomous agents. A particularly important application is the fuel optimal control for planetary soft landing, whose complete solution has been an open problem since the Apollo Moon landings. We developed a novel “lossless convexification” method of solution, which will enable the next generation planetary missions, such as Mars robotic sample return and manned missions.
Biographical Sketch: Behcet Acikmese is an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. He received his Ph.D. in Aerospace Engineering in 2002 from Purdue University. He was a Visiting Assistant Professor of Aerospace Engineering at Purdue University before joining NASA Jet Propul- sion Laboratory (JPL) in 2003. He was a senior technologist at JPL and a lecturer in GALCIT at Caltech. At JPL, Dr. Acikmese developed GN&C algorithms for planetary landing, formation flying spacecraft, and asteroid and comet sample return missions. He was the developer of the “fly-away” GN&C algorithms in Mars Science Laboratory, which successfully landed on Mars in August 2012. His convex optimization based planetary landing algorithm is currently being flight-tested for future Mars missions.
Hosted by Leszek Demkowicz