Review of Tethered Unmanned Aerial Vehicles: Building Versatile and Robust Tethered Multirotor UAV System
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Dario Handrick↗
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The Planetary Protection Strategy of the Earth Return Orbiter–Capture, Containment & Return System in the Context of the Mars Sample Return Campaign
The Mars Sample Return Campaign aims at bringing back to Earth the rock and atmospheric samples that the rover Perseverance has started to collect on the surface of Mars with the goal of analyzing them in a facility built specifically for this purpose to answer questions about the habitability of Mars. The Campaign consists of several missions, including the Earth Return Orbiter–Capture, Containment & Return System (ERO-CCRS), which will capture the samples previously put in Martian orbit, contain them in redundant containers to ensure that no unsterilized particles are released, and return them to Earth through a parachute-less entry vehicle. Both NASA and ESA policies address the United Nations’ Outer Space Treaty by addressing potential harm from material returned from solar system bodies beyond the Earth-Moon system. In the conduct of Mars Sample Return, the two agencies have agreed to apply approaches consistent with their own standards to campaign elements each provides. This work presents the overall strategy for both forward and backward planetary protection for the ERO-CCRS mission. Specifically, for forward planetary protection, CCRS is not required to meet specific bioburden requirements as a Category III mission provided the ERO (1) meets orbital lifetime requirements during orbiter operations and (2) any elements jettisoned at Mars meet orbital lifetime requirements. CCRS is required to be built in ISO-8 or better cleanrooms and, by agreement with ERO, be compatible with direct bioburden verification methods. For backward planetary protection, the overall approach includes building robust, highly reliable systems to prevent inadvertent release of unsterilized Mars material through redundant containment vessels and particle transport analyses. Ongoing work to define verification approaches and quantify containment assurance levels for specific sample return systems will also be discussed, along with how those data will inform launch approval for ERO-CCRS.
Giuseppe Cataldo↗
Planetary Surface Operations and Utilization: How ISS and Artemis Missions Can Be Used to Model Human Exploration of Mars
As NASA moves forward with plans to send astronauts to the Moon under Artemis missions and prepare for human exploration of Mars, the Agency is developing a set of high-level objectives for human spaceflight, identifying 50 points falling into four overarching categories of exploration. An element in NASA’s overall process of achieving these objectives is to leverage its assets and missions – such as the many crew increments sent to the International Space Station and future Artemis expeditions sent to the Moon – to develop more robust spaceflight systems and build a culture of interplanetary human exploration. This paper describes several examples of how NASA is exercising a process to achieve these objectives for future human Mars surface missions; both (a) building on lessons learned from ISS missions and maturing plans for Artemis missions, and (b) using human Mars mission planning to inform the plans for future ISS and Artemis missions so that the knowledge gained will reduce uncertainty and risk for Mars. One focal point for this two-way interaction between ISS and Artemis with future human Mars missions is a document titled “Reference Surface Activities for Crewed Mars Mission Systems and Utilization” (HEOMD-415), which describes the systems and operations of the crew thought necessary for the first human Mars surface mission. The details described in this paper will address three specific aspects of HEOMD-415 that have been influenced by ISS and where HEOMD-415 is influencing plans in ISS, Artemis, research and technology development, and other related aspects: (1) crew (activity planning and medical), (2) Mars surface infrastructure, and (3) communication and navigation support. The paper will close by describing near-term opportunities for tests and analogs relevant to these aspects of HEOMD-415.
Mars↗
PLANETARY SURFACE OPERATIONS AND UTILIZATION: HOW ISS AND ARTEMIS MISSIONS CAN BE USED TO MODEL HUMAN EXPLORATION OF MARS
As NASA moves forward with plans to send astronauts to the Moon under Artemis missions and prepare for human exploration of Mars, the Agency is developing a set of high-level objectives for human spaceflight, identifying 50 points falling into four overarching categories of exploration. An element in NASA’s overall process of achieving these objectives is to leverage its assets and missions – such as the many crew increments sent to the International Space Station and future Artemis expeditions sent to the Moon – to develop more robust spaceflight systems and build a culture of interplanetary human exploration. This paper describes several examples of how NASA is exercising a process to achieve these objectives for future human Mars surface missions; both (a) building on lessons learned from ISS missions and maturing plans for Artemis missions, and (b) using human Mars mission planning to inform the plans for future ISS and Artemis missions so that the knowledge gained will reduce uncertainty and risk for Mars. One focal point for this two-way interaction between ISS and Artemis with future human Mars missions is a document titled “Reference Surface Activities for Crewed Mars Mission Systems and Utilization” (HEOMD-415), which describes the systems and operations of the crew thought necessary for the first human Mars surface mission. The details described in this paper will address three specific aspects of HEOMD-415 that have been influenced by ISS and where HEOMD-415 is influencing plans in ISS, Artemis, research and technology development, and other related aspects: (1) crew (activity planning and medical), (2) Mars surface infrastructure, and (3) communication and navigation support. The paper will close by describing near-term opportunities for tests and analogs relevant to these aspects of HEOMD-415.
Mars↗
Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland
As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.
Disasters↗
Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland
As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.
Disasters↗
Decision Support Systems for Launch and Range Operations Using Jess
The virtual test bed for launch and range operations developed at NASA Ames Research Center consists of various independent expert systems advising on weather effects, toxic gas dispersions and human health risk assessment during space-flight operations. An individual dedicated server supports each expert system and the master system gather information from the dedicated servers to support the launch decision-making process. Since the test bed is based on the web system, reducing network traffic and optimizing the knowledge base is critical to its success of real-time or near real-time operations. Jess, a fast rule engine and powerful scripting environment developed at Sandia National Laboratory has been adopted to build the expert systems providing robustness and scalability. Jess also supports XML representation of knowledge base with forward and backward chaining inference mechanism. Facts added - to working memory during run-time operations facilitates analyses of multiple scenarios. Knowledge base can be distributed with one inference engine performing the inference process. This paper discusses details of the knowledge base and inference engine using Jess for a launch and range virtual test bed.
Thirumalainambi, Rajkumar↗
Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland
As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.
Erika Munshi↗
Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland
As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.
Erika Munshi↗
The AI Bus architecture for distributed knowledge-based systems
The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.
Schultz, Roger D.↗
ECLSS Four-Bed CO2 Scrubber Digital Twin
The ECLSS Digital Twin is a cloud-based simulation of the Four-Bed CO2 Scrubber, currently one of the primary means of removing carbon dioxide onboard the International Space Station, that operationalizes SME-developed multi-physics models and replicates conditions of the actual hardware in near real-time. In addition to providing insight into the system’s performance, it will enable prognostics, diagnostics, predictive maintenance, and simulated off-nominal scenarios. By leveraging digital representations, projects can save resources and gain a better understanding of their physical systems with the goal of building robust and reliable hardware for future deep space exploration. The use of data infrastructure in the cloud allows for streamlined analysis and visualization on a much larger scale than is possible with current tools.
Jared Wilson↗
Lunar Latitude and Terrain Radiator Sensitivity Study
The thermal environment on the moon is highly complex and diverse. The lunar surface near the equator develops extreme hot average temperatures during the lunar day due to solar flux vectors that are nearly orthogonal to the surface. The lunar poles have a cold and uniquely complex thermal environment with low solar elevation angles and permanently shadowed regions located just kilometers from some of the most highly illuminated regions of the moon. Likewise, the topography can range from very flat crater basins to dramatically tall features such as mountains and crater rims. Consequently, when sizing the radiators of a lunar surface vehicle, the specific thermal environment found in the targeted landing or deployment zone must be well understood to build robust appropriately scaled thermal control systems. Here described are parametric studies that characterize the sensitivity of lunar radiator performance to lunar terrain and latitude. Heat rejection is calculated for different radiator tilt angles in a variety of terrain environments. Radiator performance as a function of underside thermal condition is also characterized at lunar latitudes ranging from equatorial to polar. Impacts of latitude and terrain on radiator performance are quantified, and regions are identified where the thermal environment is more or less favorable for specific radiator designs.
Lunar Vehicle Radiator↗
Lunar Latitude and Terrain Radiator Sensitivity Study
The thermal environment on the moon is highly complex and diverse. The lunar surface near the equator develops extreme hot average temperatures during the lunar day due to solar flux vectors that are nearly orthogonal to the surface. The lunar poles have a cold and uniquely complex thermal environment with low solar elevation angles and permanently shadowed regions located just kilometers from some of the most highly illuminated regions of the moon. Likewise, the topography can range from very flat crater basins to dramatically tall features such as mountains and crater rims. Consequently, when sizing the radiators of a lunar surface vehicle, the specific thermal environment found in the targeted landing or deployment zone must be well understood to build robust, appropriately scaled thermal control systems. Here described are parametric studies that characterize the sensitivity of lunar radiator performance to lunar terrain and latitude. Heat rejection is calculated for different radiator tilt angles in a variety of terrain environments. Radiator performance as a function of underside thermal condition is also characterized at lunar latitudes ranging from equatorial to polar. Impacts of latitude and terrain on radiator performance are quantified, and regions are identified where the thermal environment is more or less favorable for specific radiator designs.
Lunar Vehicle Radiator↗
Lunar Latitude and Terrain Radiator Sensitivity Study
The thermal environment on the moon is highly complex and diverse. The lunar surface near the equator develops extreme hot average temperatures during the lunar day due to solar flux vectors that are nearly orthogonal to the surface. The lunar poles have a cold and uniquely complex thermal environment with low solar elevation angles and permanently shadowed regions located just kilometers from some of the most highly illuminated regions of the moon. Likewise, the topography can range from very flat crater basins to dramatically tall features such as mountains and crater rims. Consequently, when sizing the radiators of a lunar surface vehicle, the specific thermal environment found in the targeted landing or deployment zone must be well understood to build robust, appropriately scaled thermal control systems. Here described are parametric studies that characterize the sensitivity of lunar radiator performance to lunar terrain and latitude. Heat rejection is calculated for different radiator tilt angles in a variety of terrain environments. Radiator performance as a function of underside thermal condition is also characterized at lunar latitudes ranging from equatorial to polar. Impacts of latitude and terrain on radiator performance are quantified, and regions are identified where the thermal environment is more or less favorable for specific radiator designs.
Lunar Thermal Analysis↗
Lunar Latitude and Terrain Radiator Sensitivity Study
The thermal environment on the moon is highly complex and diverse. The lunar surface near the equator develops extreme hot average temperatures during the lunar day due to solar flux vectors that are nearly orthogonal to the surface. The lunar poles have a cold and uniquely complex thermal environment with low solar elevation angles and permanently shadowed regions located just kilometers from some of the most highly illuminated regions of the moon. Likewise, the topography can range from very flat crater basins to dramatically tall features such as mountains and crater rims. Consequently, when sizing the radiators of a lunar surface vehicle, the specific thermal environment found in the targeted landing or deployment zone must be well understood to build robust appropriately scaled thermal control systems. Here described are parametric studies that characterize the sensitivity of lunar radiator performance to lunar terrain and latitude. Heat rejection is calculated for different radiator tilt angles in a variety of terrain environments. Radiator performance as a function of underside thermal condition is also characterized at lunar latitudes ranging from equatorial to polar. Impacts of latitude and terrain on radiator performance are quantified, and regions are identified where the thermal environment is more or less favorable for specific radiator designs.
Lunar Vehicle Radiator↗
Verifying Stability of Dynamic Soft-Computing Systems
Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.
Wen, Wu↗
Cumulus Lessons Learned: Building, Testing, and Sharing a Cloud Archive
Cumulus is a scalable, extensible cloud-based archive system which is capable of ingesting, archiving, and distributing data from both existing on-prem sources and new cloud-native missions. As we have built and evolved the system with contributions from seven NASA EOSDIS organizations, we have learned several lessons about how to build a robust, broadly-applicable, microservices-based cloud system for geospatial data which we will share in this talk.
Quinn, Patrick↗
Methodologies for building robust schedules
COMPASS is the name of a Computer Aided Scheduling System designed and built for NASA. COMPASS can be used to develop schedule of activities based upon the temporal relationships of the activities and their resource requirements. COMPASS uses this information, and guided by the user, develops precise start and stop times for the activities. In actual practice however, it is impossible to know with complete certainty what the actual durations of the scheduled activities will really be. The best that one can hope for is knowledge of the probability distribution for the durations. This paper investigates methodologies for using a scheduling tool like COMPASS that is based upon definite values for the resource requirements, while building schedules that remain valid in the face of the schedule execution perturbations. Representations for the schedules developed by these methodologies are presented, along with a discussion of the algorithm that could be used by a computer onboard a spacecraft to efficiently monitor and execute these schedules.
Dean, John H.↗