Mathematical Optimization of Resolution Improvement in Structured Light data by Periodic Scanning Motion: Application for Feedback during Lunar Landing
<|reference_start|>Mathematical Optimization of Resolution Improvement in Structured Light data by Periodic Scanning Motion: Application for Feedback during Lunar Landing: This research explores the enhancement of lunar landing precision through an advanced structured light system, integrating machine learning, Iterative Learning Control (ILC) and Structured Illumination Microscopy (SIM) techniques. By employing Moire fringe patterns for high-precision scanning maneuvers, the study addresses the limitations of conventional structured light systems. A nonlinear mathematical optimization model is developed to refine the world model, optimizing oscillation frequency and amplitude to improve resolution. The findings suggest that this approach can double the conventional resolution, promising significant advancements in the accuracy of lunar landings, with potential real-time application.<|reference_end|>
@article{elsharhawy2024mathematical,
    title={Mathematical Optimization of Resolution Improvement in Structured Light
  data by Periodic Scanning Motion: Application for Feedback during Lunar
  Landing},
    author={Tarek A. Elsharhawy, P. James Schuck, Shuo Liu and Luc Saikali},
    journal={arXiv preprint arXiv:2408.06628},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06628},
    primaryClass={eess.SY cs.SY}
}