Abstract. Many future engineering applications will require dramatic increases in our existing Autonomous GN&C (Guidance, Navigation and Control) capabilities. These include robotic sample return missions to planets, comets, and asteroids, formation flying spacecraft 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 under severe mission constraints and in the presence of 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 class of problems can be solved quickly to global optimality 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 GN&C challenges of advanced autonomous systems.

Our research has provided new analytical results that enabled the formulation of many autonomous GN&C problems in a convex optimization framework. This presentation introduces 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.