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Meeting	ACGS Committee Meeting 130 - Missoula, MT - March 2023
Agenda Location	6 SUBCOMMITTEE B – MISSILES AND SPACE 6.4 DQG: Dual Quaternion-based 6-DoF Powered-Descent Guidance for the NASA SPLICE Program
Title	DQG: Dual Quaternion-based 6-DoF Powered-Descent Guidance for the NASA SPLICE Program
Presenter	Abhinav Kamath
Affiliation	University of Washington
Available Downloads*	presentation
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Abstract	NASA has deemed precision landing and hazard avoidance (PL&HA) a high-priority capability to facilitate missions of exploration to celestial bodies in the solar system. Technologies such as terrain relative navigation (TRN) and hazard detection and avoidance (HDA) require constraining the flight envelope in a manner that couples the translation and attitude states. One such important constraint is the distance-triggered sensor-pointing constraint, which requires a body-fixed sensor to point at a feature of interest on the surface when the descent vehicle is within a specified range of distances to the target, in order to enable accurate scans and enable large diverts if necessary. DQG, originally developed by Reynolds et al., is a 6-DoF powered-descent guidance algorithm that is not only able to handle such constraints, but is also amenable to real-time implementation onboard computationally constrained flight hardware. It was chosen as the candidate powered-descent guidance algorithm for the Safe and Precise Landing – Integrated Capabilities Evolution (SPLICE) project, and has been open-loop flight-tested on the Blue Origin New Shepard suborbital rocket onboard the descent and landing computer (DLC). In order to tackle the challenges of execution speed and code footprint, we develop a new optimization framework called sequential conic optimization (SeCO) that blends together sequential convex programming (high-level) and first-order conic optimization (low-level), while being entirely devoid of matrix factorizations and inversions. This framework uses PIPG (proportional-integral projected gradient), a high-performance first-order solver that effectively exploits the structure of trajectory optimization problems—thus making it amenable to customization, which in turn leads to faster execution. Furthermore, the solver only performs simple linear algebra operations such as matrix-vector multiplication and vector addition operations, and thus lends to easy verification and validation and a small code footprint. All of these features make it well-suited for real-time, onboard guidance. Our recent work on 3-DoF Mars landing guidance (convex) and multi-phase landing guidance for a Starship-like vehicle (nonconvex) has demonstrated that this solver is capable of solving optimal control problems in a matter of milliseconds, and is significantly faster than other state-of-the-art solvers.