The dual quaternion guidance (DQG) algorithm was selected as the candidate six-degree-of-freedom (6-DoF) powered-descent guidance algorithm for NASA’s Safe and Precise Landing — Integrated Capabilities Evolution (SPLICE) project. DQG is capable of handling state-triggered constraints that are of the utmost importance in terms of enabling technologies such as terrain relative navigation (TRN). In this work, we develop a custom solver for DQG to enable onboard implementation for future rocket landing missions. We describe the design and implementation of a real-time-capable optimization framework, called sequential conic optimization (SeCO), that blends together sequential convex programming and first-order conic optimization to solve difficult nonconvex trajectory optimization problems, such as DQG, in real-time. This framework is entirely devoid of matrix factorizations/inversions, making it suitable for safety-critical applications. Under the hood, the SeCO framework leverages a first-order primal-dual conic optimization solver, based on the proportional-integral projected gradient method (PIPG), that combines the ideas of projected gradient descent and proportional-integral feedback of constraint violation. Unlike other conic optimization solvers, PIPG effectively exploits the sparsity structure and geometry of the constraints, avoids expensive equation-solving, and is suitable for both real-time and largescale applications. We describe the implementation of this solver, and develop customizable first-order methods, including a mostly-analytical preconditioning algorithm, to solve the nonconvex DQG optimal control problem in real-time. Strategies such as warm-starting and extrapolation are leveraged to further accelerate convergence. We show that the DQG-customized subproblem solver is able to solve the problem significantly faster than other state-of-the-art convex optimization solvers. Finally, in preparation for an upcoming closed-loop flight test campaign, we test our custom solver onboard the NASA SPLICE Descent and Landing Computer (DLC) in a hardware-in-the-loop setting. We observe that our algorithm is significantly faster than previously reported solve-times using the flight-tested interior point method (IPM)-based subproblem solver, BSOCP. Furthermore, our custom solver meets (and exceeds) NASA’s autonomous precision rocket-landing guidance update-rate requirements for the first time, thus demonstrating the viability of SeCO for real-time, mission-critical applications onboard computationally constrained flight hardware.
Onboard Dual Quaternion Guidance for Rocket Landing
14.07.2025
Preprint
Keine Angabe
Englisch