Development of a Signature-based Terrain Relative Navigation System for Precision Landing

Owens, Chris; Macdonald, Kori; Hardy, Jeremy; Lindsay, R.O.; Redfield, Morgan; Bloom, Michael; Bailey, Erik S.; Cheng, Yang; Clouse, Daniel S.; Villalpando, Carlos Y.; Hambardzumyan, Ashot; Johnson, Andrew; Horchler, Andrew D.

doi:10.2514/6.2021-0376

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Newer approaches incorporate hazard footprint and spacecraft geometry but demand substantial computational power [33]. With numerous missions planned for the Moon and beyond, safe landing systems have been developed and implemented, especially for the upcoming CLPS missions [34][35][36] scheduled for 2024. These missions will collect valuable data to validate HDA algorithms based on cameras.…”

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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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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“…For improving the horizontal-position estimation of range-velocity-updated inertial navigation, image-aided navigation has been investigated for years [3,4]. Not until the Perseverance Mars landing mission was terrain-relative navigation utilized in actual Mars landing flight for precision landing [5]. Regarding guidance for planetary PDL, researchers have investigated optimization guidance based on the pseudo-spectral method and a convex programming approach for Mars huangxyhit@sina.com landing [6,7].…”

Section: Introductionmentioning

confidence: 99%

Powered-descent landing GNC system design and flight results for Tianwen-1 mission

Huang¹,

Xu²,

Hu³

et al. 2022

Astrodyn

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The powered-descent landing (PDL) phase of the Tianwen-1 mission began with composite backshell—parachute (CBP) separation and ended with landing-rover touchdown. The main tasks of this phase were to reduce the velocity of the lander, perform the avoidance maneuver, and guarantee a soft touchdown. The PDL phase overcame many challenges: performing the divert maneuver to avoid collision with the CBP while simultaneously avoiding large-scale hazards; slowing the descent from approximately 95 to 0 m/s; performing the precise hazard-avoidance maneuver; and placing the lander gently and safely on the surface of Mars. The architecture and algorithms of the guidance, navigation, and control system for the PDL phase were designed; its execution resulted in Tianwen-1’s successful touchdown in the morning of 15 May 2021. Consequently, the Tianwen-1 mission achieved a historic autonomous landing with simultaneous hazard and CBP avoidance.

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“…Projects such as the Russian LUNA 27 lander [3], NASA's Artemis [31] and VIPER [12] programmes, ESA's PRO-SPECT mission [38], JAXA's SLIM [30], the Indian Chandrayaan 3 [16], and China's Chang'e 6 [10] are expected to land on the Moon before the end of the decade. Furthermore, commercial missions by private companies like Astrobotic Technology [33], SpaceX [41], and Blue Origin [11] are also scheduled to land and perform experiments on the lunar surface in the near future. Several of these projects and experiments aim to test and develop in situ resource utilisation (ISRU) technologies, which could allow the utilisation of native lunar material.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to the analytical modelling of an auger conveyor system for lunar regolith transportation

et al. 2022

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Auger-based transportation systems are a promising method to transport lunar regolith for in situ resource utilisation. An analytical model based on terrestrial auger conveyor industry guidelines is used to predict the behaviour and performance parameters of an auger conveyor system under a range of initial conditions. Key aspects of the model have been validated with published experimental data. The proposed model produces more accurate predictions than previous methods and calculates the inclination angle with the best conveying efficiency. The proposed model output flow predictions have on average $$47\%$$ 47 % less deviation from the experimental data mean than previous model predictions, while the predictions for power requirements without considering energy losses present 42.9% and $$59.2\%$$ 59.2 % less deviation than previous predictions. When the losses are considered, the proposed model predictions are 70% and $$86.4\%$$ 86.4 % more accurate than the previous models, which have been found to underestimate the power requirements of this type of conveyors.

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Development of a Signature-based Terrain Relative Navigation System for Precision Landing

Cited by 11 publications

References 10 publications

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

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

Powered-descent landing GNC system design and flight results for Tianwen-1 mission

A novel approach to the analytical modelling of an auger conveyor system for lunar regolith transportation

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