In many autonomous navigation tasks, agents must make decisions based on perceptual information. Enhancing information gained from observations enables agents to achieve goals more efficiently, especially in time- and fuel-critical situations like Powered Descent Guidance. This paper proposes a trajectory optimization framework that promotes information maximization. We employ Gaussian process regression to model the landing space topography, providing surface estimates and their covariance. This covariance integral is used as a cost in an SCP-based trajectory optimization problem, allowing the agent to plan trajectories that minimize environmental covariance. We demonstrate our algorithm in a high-fidelity simulation using Unreal Engine with Digital Elevation Models captured by the Lunar Reconnaissance Orbiter.
@inbook{doi:10.2514/6.2025-1896,
author = {Christopher R. Hayner and Natalia Pavlasek and Karen Leung and Behcet Acikmese and John M. Carson},
title = {Information-Aware Powered Descent Guidance for Entry, Descent and Landing},
booktitle = {AIAA SCITECH 2025 Forum},
doi = {10.2514/6.2025-1896},
URL = {https://arc.aiaa.org/doi/abs/10.2514/6.2025-1896},
eprint = {https://arc.aiaa.org/doi/pdf/10.2514/6.2025-1896}