Next-Generation NASA Hazard Detection System Development

Restrepo, Carolina I.; Chen, Po-Ting; Sostaric, Ronald R.; Carson, John M.

doi:10.2514/6.2020-0368

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Figure 3 illustrates an example of different light conditions a lunar landscape can experience during different seasons. For this reason, some of the state-of-the-art hazard detection and avoidance methods have transitioned to rely on light detection and ranging (LiDAR) approaches, as the laser allows for the quick scanning and rebuilding of surfaces with high quality and robustness to variable light conditions [15,16]. However, no completely LiDAR systems have flown or achieved NASA's Technology Readiness Level due to size, weight, and power consumption constraints.…”

Section: Lunar Light Plainsmentioning

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: Lunar Light Plainsmentioning

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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“…Therefore, regions where safety value could not be calculated appear in the safety map which leads to fewer safe regions in the map. 29 In this paper, this effect was simply modeled by assigning unsafe labels to pixels with slant angles between the lander smaller than 70 degrees.…”

Section: Observation Data Generationmentioning

confidence: 99%

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

Iiyama,

Tomita,

Jagatia

et al. 2021

Preprint

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This research proposes a new integrated framework for identifying safe landing locations and planning in-flight divert maneuvers. The state-of-the-art algorithms for landing zone selection utilize local terrain features such as slopes and roughness to judge the safety and priority of the landing point. However, when there are additional chances of observation and diverting in the future, these algorithms are not able to evaluate the safety of the decision itself to target the selected landing point considering the overall descent trajectory. In response to this challenge, we propose a reinforcement learning framework that optimizes a landing site selection strategy concurrently with a guidance and control strategy to the target landing site. The trained agent could evaluate and select landing sites with explicit consideration of the terrain features, quality of future observations, and control to achieve a safe and efficient landing trajectory at a system-level. The proposed framework was able to achieve 94.8 % of successful landing in highly challenging landing sites where over 80% of the area around the initial target lading point is hazardous, by effectively updating the target landing site and feedback control gain during descent.

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“…However, performance of the algorithm can degrade with increased sensor noised, navigation error, and missing data. Building off of the success of the ALHAT project and Morpheus experiments, next generation hazard systems are currently under development through NASA's Safe & Precise Landing -Integrated Capabilities Evolution (SPLICE) program [22].…”

Section: A Autonomous Hazard Detectionmentioning

confidence: 99%

“…Table 1 provides the sensor parameters of the simulated sensor. These parameters are selected to obtain DEMs with similar dimensions and characteristics of DEMs obtained through real and simulated experiments with FLASH LiDAR surveys for imaging planetary surfaces [22,[44][45][46]. For the simulation, the sensor is moved across the terrain at a fixed altitude of 500 m to collect incremental scans of the terrain.…”

Section: Simulated Terrain Datamentioning

confidence: 99%

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Tomita¹,

Skinner²,

Ho³

2021

Preprint

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Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. At the same time, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has been often limited due to concerns regarding their outputs' reliability. In response to this background, this paper proposes an uncertaintyaware learning-based method for hazard detection and landing site selection. The developed approach enables reliable safe landing site selection by: (i) generating a safety prediction map and its uncertainty map together via Bayesian deep learning and semantic segmentation; and (ii) using the generated uncertainty map to filter out the uncertain pixels in the prediction map so that the safe landing site selection is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model and varying noise levels to demonstrate the performance of the proposed approach.

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Next-Generation NASA Hazard Detection System Development

Cited by 10 publications

References 19 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

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

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