Influence of Trajectory on Accuracy of Hazard Estimation During Lunar Landing

Crane, Eleanor S.; Rock, Stephen M.

doi:10.2514/6.2012-4554

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Ref. 57 documents that such a "fly-over" trajectory is better suited for HDA than a direct approach. The thrust components commanded by guidance and the resulting reduction in velocity are shown in Figures 9 (b) and (d).…”

Section: Moon Low-speed Approachmentioning

confidence: 97%

Guidance for Autonomous Precision Landing on Atmosphereless Bodies

Gerth

Mooij

2014

AIAA Guidance, Navigation, and Control Conference

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Two key technologies that will likely fly on next-generation planetary landers are hazard detection and avoidance (HDA) and vision-based navigation. The purpose of HDA is to make previously unsafe landing sites accessible by future vehicles, and to increase the safe landing probability overall. Vision-based navigation will enable to target specific landing sites more precisely and also plays an important role for HDA. This paper formulates requirements on the guidance system that derive from the needs of these new systems. After the introduction of a reference mission scenario with HDA and vision-based navigation inthe-loop, an extensive literature survey of the state-of-the-art in approach-phase guidancealgorithms is presented. This presents the methods under the angle of HDA-compatibility. The sheer number of papers mentioned here leads to the conclusion that trade-offs are challenging. It is recommended to do a down-selection from the papers presented here, and do a performance-based trade study for the particular test case under consideration with a few candidates. As an example, E-Guidance is presented for a Mercury-and a Moon-landing scenario. Because the results differ dramatically, it is concluded that it is essential to perform trade-offs on the actual mission scenario under study, and that trades cannot merely be based on references in the literature. Because the proposed E-Guidance method is not satisfyingly robust, it is a goal for future research to improve upon this.

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Section: Moon Low-speed Approachmentioning

confidence: 97%

Guidance for Autonomous Precision Landing on Atmosphereless Bodies

Gerth

Mooij

2014

AIAA Guidance, Navigation, and Control Conference

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“…It develops an information-seeking control strategy that adapts the landing trajectory to improve visual data collection for hazard mapping. It uses a monocular camera for its suitability for missions with strict mass and power constraints [25].…”

Section: Hazard-detection and Avoidancementioning

confidence: 99%

Dense Feature Matching for Hazard Detection and Avoidance Using Machine Learning in Complex Unstructured Scenarios

Posada,

Henderson

2024

Aerospace

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Exploring the Moon and Mars are crucial steps in advancing space exploration. Numerous missions aim to land and research in various lunar locations, some of which possess challenging surfaces with unchanging features. Some of these areas are cataloged as lunar light plains. Their main characteristics are that they are almost featureless and reflect more light than other lunar surfaces. This poses a challenge during navigation and landing. This paper compares traditional feature matching techniques, specifically scale-invariant feature transform and the oriented FAST and rotated BRIEF, and novel machine learning approaches for dense feature matching in challenging, unstructured scenarios, focusing on lunar light plains. Traditional feature detection methods often need help in environments characterized by uniform terrain and unique lighting conditions, where unique, distinguishable features are rare. Our study addresses these challenges and underscores the robustness of machine learning. The methodology involves an experimental analysis using images that mimic lunar-like landscapes, representing these light plains, to generate and compare feature maps derived from traditional and learning-based methods. These maps are evaluated based on their density and accuracy, which are critical for effective structure-from-motion reconstruction commonly utilized in navigation for landing. The results demonstrate that machine learning techniques enhance feature detection and matching, providing more intricate representations of environments with sparse features. This improvement indicates a significant potential for machine learning to boost hazard detection and avoidance in space exploration and other complex applications.

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Terrain Relative Navigation for Planetary Landing Using Stereo Vision Measurements Obtained from Hazard Mapping

Woicke

Mooij

2017

Advances in Aerospace Guidance, Navigation and Control

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As a result of new aviation legislation, from 2019 on all air-carrier pilots are obliged to go through flight simulator-based stall recovery training. For this reason the Control and Simulation division at Delft University of Technology has set up a task force to develop a new methodology for high-fidelity aircraft stall behavior modeling and simulation. As part of this research project, the development of a new high-fidelity Cessna II simulation model, valid throughout the normal, pre-stall flight envelope, is presented in this paper. From an extensive collection of flight test data, aerodynamic model identification was performed using the Two-Step Method. New in this approach is the use of the Unscented Kalman Filter for an improved accuracy and robustness of the state estimation step. Also, for the first time an explicit data-driven model structure selection is presented for the Citation II by making use of an orthogonal regression scheme. This procedure has indicated that most of the six non-dimensional forces and moments can be parametrized sufficiently by a linear model structure. It was shown that only the translational and lateral aerodynamic force models would benefit from the addition of higher order terms, more specifically the squared angle of attack and angle of sideslip. The newly identified aerodynamic model was implemented into an upgraded version of the existing simulation framework and will serve as a basis for the integration of a stall and post-stall model.

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Influence of Trajectory on Accuracy of Hazard Estimation During Lunar Landing

Cited by 4 publications

References 16 publications

Guidance for Autonomous Precision Landing on Atmosphereless Bodies

Guidance for Autonomous Precision Landing on Atmosphereless Bodies

Dense Feature Matching for Hazard Detection and Avoidance Using Machine Learning in Complex Unstructured Scenarios

Terrain Relative Navigation for Planetary Landing Using Stereo Vision Measurements Obtained from Hazard Mapping

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