Trajectory Optimization for Lunar Landing

2024

1. SciTech

   

   Constrained Visibility Guidance for 6-DOF Powered Descent Maneuvers with Terrain Scanning using Sequential Convex Programming

   Samuel C Buckner, Joshua Shaffer, John M Carson, and 3 more authors

   In AIAA SciTech 2024 Forum, 2024

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   Recent advances in perceptive sensors and computer vision have motivated new formulations for powered descent guidance, wherein a vehicle must perform a pinpoint landing on a celestial body while simultaneously conducting close-range scans of the landing environment to detect and avoid potentially-unsafe hazards. Furthermore, mission plans may necessitate exploration and scouting of the environment to determine candidate landing sites in real time. In this paper, a novel six degree-of-freedom (6-DOF) optimal control formulation is presented to model visibility-based constraints such that line-of-sight to a circular ground-based region of interest is guaranteed, up to a specified discrete temporal resolution, with an accommodating theory of constrained conic intersections introduced to support this approach. This formulation, termed Constrained Visibility Guidance (CVG), further leverages and extends theory in sequential convex programming and state-triggered constraints to enable mission-practical constraint specification and transformation of a highly-nonconvex problem into one that can be iteratively solved with modern second-order cone program solvers. Ultimately, CVG is shown to be highly performant in terms of solve time and convergence properties, even under complex and highly-constrained problem design. Numerical simulation results are presented to validate these claims.

   @inproceedings{buckner2024constrained,
     title = {Constrained Visibility Guidance for 6-DOF Powered Descent Maneuvers with Terrain Scanning using Sequential Convex Programming},
     author = {Buckner, Samuel C and Shaffer, Joshua and Carson, John M and Johnson, Breanna J and Sostaric, Ronald R and Acikmese, Behcet},
     booktitle = {AIAA SciTech 2024 Forum},
     pages = {1759},
     year = {2024},
     url = {https://arc.aiaa.org/doi/abs/10.2514/6.2024-1759},
     dimensions = {true}
   }

2. SciTech

   

   Implementation and Testing of Convex Optimization-based Guidance for Hazard Detection and Avoidance on a Lunar Lander

   Joshua Shaffer, Chris Owens, Theresa Klein, and 5 more authors

   In AIAA SciTech 2024 Forum, 2024

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   Lunar logistics companies have experienced growth over the past few years, with interest focused on delivering payloads to hazardous areas like the South Pole. Achieving safe touchdowns while meeting precise landing requirements necessitates more and more advanced perception sensor suites and in-situ data collection of the environments. One such sensor includes hazard detection LIDARs, which typically constrain trajectories to meet specific range, velocity, time, and field of view requirements for data collection. Creating a real-time guidance solution to meet these constraints has motivated past research on solvers that use convex formulations and embed these constraints directly in the problem formulation. Maturation of these approaches has in turn motivated the testing and implementation for use in-flight and onboard a lander. While more advanced 6-DOF approaches exist, 3-DOF formulations are attractive due to their simpler implementation and faster solve times. In this paper, a 3-DOF solution for meeting convex hazard detection and avoidance constraints is presented, followed by implementation and 6-DOF testing results for both a software-in-the-loop Monte Carlo platform and a real-time hardware-in-the-loop flight processor platform. In addition, a novel constraint formulation is introduced, known as the control-robust envelope, which enables guaranteed satisfaction of hazard detection objectives. Results show that the implementation approach works in both meeting the trajectory constraints for hazard detection and avoidance and is achievable in real-time on the flight platform.

   @inproceedings{shaffer2024implementation,
     title = {Implementation and Testing of Convex Optimization-based Guidance for Hazard Detection and Avoidance on a Lunar Lander},
     author = {Shaffer, Joshua and Owens, Chris and Klein, Theresa and Horchler, Andrew D and Buckner, Samuel C and Johnson, Breanna J and Carson, John M and Acikmese, Behcet},
     booktitle = {AIAA SciTech 2024 Forum},
     pages = {1584},
     year = {2024},
     url = {https://arc.aiaa.org/doi/10.2514/6.2024-1584},
     dimensions = {true}
   }