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NASA NTRS2014

NASA Space Rocket Logistics Challenges

The Space Launch System (SLS) is the new NASA heavy lift launch vehicle and is scheduled for its first mission in 2017. The goal of the first mission, which will be uncrewed, is to demonstrate the integrated system performance of the SLS rocket and spacecraft before a crewed flight in 2021. SLS has many of the same logistics challenges as any other large scale program. Common logistics concerns for SLS include integration of discreet programs geographically separated, multiple prime contractors with distinct and different goals, schedule pressures and funding constraints. However, SLS also faces unique challenges. The new program is a confluence of new hardware and heritage, with heritage hardware constituting seventy-five percent of the program. This unique approach to design makes logistics concerns such as commonality especially problematic. Additionally, a very low manifest rate of one flight every four years makes logistics comparatively expensive. That, along with the SLS architecture being developed using a block upgrade evolutionary approach, exacerbates long-range planning for supportability considerations. These common and unique logistics challenges must be clearly identified and tackled to allow SLS to have a successful program. This paper will address the common and unique challenges facing the SLS programs, along with the analysis and decisions the NASA Logistics engineers are making to mitigate the threats posed by each.

Neeley, James R.↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRS2008

Mars Design Reference Architecture 5.0 Study: Executive Summary

The NASA Mars Design Reference Architecture 5.0 Study seeks to update its long term goals and objective for human exploration missions; flight and surface systems for human missions and supporting infrastructure; operational concept for human and robotic exploration of Mars; key challenges including risk and cost drivers; and, its development schedule options. It additionally seeks to assess strategic linkages between lunar and Mars strategies and develop and understanding of methods for reducing the cost/risk of human Mars missions through investment in research, technology development, and synergy with other exploration plans. Recommendations are made regarding conjunction class (long-stay) missions which are seen as providing the best balance of cost, risk, and performance. Additionally, this study reviews entry, descent, and landing challenges; in-space transportation systems; launch vehicle and Orion assessments; risk and risk mitigation; key driving requirements and challenges; and, lunar linkages.

Drake, Bret G.↗
NASA NTRS2011

Contact Graph Routing

Contact Graph Routing (CGR) is a dynamic routing system that computes routes through a time-varying topology of scheduled communication contacts in a network based on the DTN (Delay-Tolerant Networking) architecture. It is designed to enable dynamic selection of data transmission routes in a space network based on DTN. This dynamic responsiveness in route computation should be significantly more effective and less expensive than static routing, increasing total data return while at the same time reducing mission operations cost and risk. The basic strategy of CGR is to take advantage of the fact that, since flight mission communication operations are planned in detail, the communication routes between any pair of bundle agents in a population of nodes that have all been informed of one another's plans can be inferred from those plans rather than discovered via dialogue (which is impractical over long one-way-light-time space links). Messages that convey this planning information are used to construct contact graphs (time-varying models of network connectivity) from which CGR automatically computes efficient routes for bundles. Automatic route selection increases the flexibility and resilience of the space network, simplifying cross-support and reducing mission management costs. Note that there are no routing tables in Contact Graph Routing. The best route for a bundle destined for a given node may routinely be different from the best route for a different bundle destined for the same node, depending on bundle priority, bundle expiration time, and changes in the current lengths of transmission queues for neighboring nodes; routes must be computed individually for each bundle, from the Bundle Protocol agent's current network connectivity model for the bundle s destination node (the contact graph). Clearly this places a premium on optimizing the implementation of the route computation algorithm. The scalability of CGR to very large networks remains a research topic. The information carried by CGR contact plan messages is useful not only for dynamic route computation, but also for the implementation of rate control, congestion forecasting, transmission episode initiation and termination, timeout interval computation, and retransmission timer suspension and resumption.

Burleigh, Scott C.↗
NASA NTRS2011

Technology Applications that Support Space Exploration

Several enabling technologies have been identified that would provide significant benefits for future space exploration. In-Space demonstrations should be chosen so that these technologies will have a timely opportunity to improve efficiencies and reduce risks for future spaceflight. An early window exists to conduct ground and flight demonstrations that make use of existing assets that were developed for the Space Shuttle and the Constellation programs. The work could be mostly performed using residual program civil servants, existing facilities and current commercial launch capabilities. Partnering these abilities with the emerging commercial sector, along with other government agencies, academia and with international partners would provide an affordable and timely approach to get the launch costs down for these payloads, while increasing the derived benefits to a larger community. There is a wide scope of varied technologies that are being considered to help future space exploration. However, the cost and schedule would be prohibitive to demonstrate all these in the near term. Determining which technologies would yield the best return in meeting our future space needs is critical to building an achievable Space Architecture that allows exploration beyond Low Earth Orbit. The best mix of technologies is clearly to be based on our future needs, but also must take into account the availability of existing assets and supporting partners. Selecting those technologies that have complimentary applications will provide the most knowledge, with reasonable cost, for future use The plan is to develop those applications that not only mature the technology but actually perform a useful task or mission. These might include such functions as satellite servicing, a propulsion stage, processing lunar regolith, generating and transmitting solar power, cryogenic fluid transfer and storage and artificial gravity. Applications have been selected for assessment for future consideration and are addressed in this paper. These applications have been made available to the various NASA study groups that are determining the next steps the Agency must take to secure a sound foundation for future space exploration The paper also addresses how follow-on demonstrations, as launch performance grows, can build on the earlier applications to provide increased benefits for both the commercial and scientific communities. The architecture of incrementally building upon previous successes and insights dramatically lowers the overall associated risk for developing and maturing the key enabling technologies. The goal is to establish a potential business case that encourages commercial activity, thereby reducing the cost for the demonstration while using the technology maturation in developing readiness for future space exploration with overall less risk.

Henderson, Edward M.↗
NASA NTRS2018

Innovative Development of a Cross-Center Timeline Planning Tool

The Payload Operations Integration Center (POIC) at Marshall Space Flight Center (MSFC) supports planning, coordination and scheduling of science activities for the International Space Station (ISS) in coordination with other NASA centers, international partners, and payload developers. The ability to efficiently plan and re-plan in response to change is critical to the flight planning teams. With the achievement of supporting a fourth crew member aboard the ISS and an increasing amount of payload science activities, came the need for a dynamic, more efficient way of building timeline planning reports that could be readily updated as fast as payload science plans could change. This paper addresses software architecture considerations in the successful cross-center development of an automated planning tool with multiple data sources. It also discusses the practical implementation of a time-boxed, hybrid Agile Software Development (ASD) approach to deliver customer-driven value despite changing requirements with respect to low-Earth orbit operational planning activities. The goal of this paper is to open discussion with members of the international community and trade effective strategies for cross-center architectural and customer-developer driven collaborations, to support increasing utilization of planning and conducting science activities in space.

Pedoto, Ramon W.↗
NASA NTRSDate not supplied

Standards Roadmapping for Mission Assurance in Commercial Spaceflight

This paper explores the application of established roadmapping approaches in standards planning and roadmap development to support mission assurance objectives. Mission assurance requires proactive planning across multiple domains including technical, schedule, cost, organizational, and policy. The intersection of these domains happens at the enterprise level, and enterprise systems engineering processes can systematically guide mission assurance activities, including development of standards roadmaps. Standards planning and development is a critical contributor to an effective mission assurance strategy, although standards are often developed and adopted reactively. Technology and standards planning is a key Enterprise Process Management activity, as identified by the Systems Engineering Body of Knowledge, and technology and standards roadmaps are strategic tools used to enable that process. Technology roadmapping has proven effective in guiding enterprise architecture and concept design, and standards planning supports enterprise requirements definition and management. However, there are limited methodologies to guide such standards planning activities. The application of technology roadmapping principles to standards planning formalizes the process and establishes a repeatable framework for standards development and integration. The framework development described in this paper fills a methodological gap by adapting proven roadmapping techniques to standards planning. Through a comparison of roadmapping methodologies and a case study analysis, this approach outlines a systematic process for anticipating and planning standards needs. In the case of developing a standards integration roadmap for NASA’s Office of Safety and Mission Assurance (OSMA), technology roadmapping principles were adapted to develop a roadmap framework to allow NASA and OSMA to define an agency-wide standards integration plan for commercial and industry safety and mission assurance standards. The outcome highlights key adaptations required for roadmapping in a standards context and describes the process steps to formalize a standards planning activity. The roadmapping framework adapted for standards development and integration enables more strategic, forward-looking standards planning and allows for better alignment between evolving capabilities and mission assurance requirements. Beyond its application to mission assurance, this methodology can be transferred to other domains requiring standards development, providing a systematic approach that integrates systems engineering principles into policy strategy and decision-making processes.

Enterprise Systems Engineering↗
NASA NTRS1990

Automated Platform Management System Scheduling

The Platform Management System was established to coordinate the operation of platform systems and instruments. The management functions are split between ground and space components. Since platforms are to be out of contact with the ground more than the manned base, the on-board functions are required to be more autonomous than those of the manned base. Under this concept, automated replanning and rescheduling, including on-board real-time schedule maintenance and schedule repair, are required to effectively and efficiently meet Space Station Freedom mission goals. In a FY88 study, we developed several promising alternatives for automated platform planning and scheduling. We recommended both a specific alternative and a phased approach to automated platform resource scheduling. Our recommended alternative was based upon use of exactly the same scheduling engine in both ground and space components of the platform management system. Our phased approach recommendation was based upon evolutionary development of the platform. In the past year, we developed platform scheduler requirements and implemented a rapid prototype of a baseline platform scheduler. Presently we are rehosting this platform scheduler rapid prototype and integrating the scheduler prototype into two Goddard Space Flight Center testbeds, as the ground scheduler in the Scheduling Concepts, Architectures, and Networks Testbed and as the on-board scheduler in the Platform Management System Testbed. Using these testbeds, we will investigate rescheduling issues, evaluate operational performance and enhance the platform scheduler prototype to demonstrate our evolutionary approach to automated platform scheduling. The work described in this paper was performed prior to Space Station Freedom rephasing, transfer of platform responsibility to Code E, and other recently discussed changes. We neither speculate on these changes nor attempt to predict the impact of the final decisions. As a consequence some of our work and results may be outdated when this paper is published.

Hull, Larry G.↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repair Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, understanding how crew repair and maintenance timelines are impacted by mission operations and technology changes is vital for future mission planning. Past work has been done using historical International Space Station (ISS) data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available datasets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat (SH), and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA ISS maintenance and crew time data are logged into two central databases: the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS). Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair time for each failure and repair event. The entire maintenance activity dataset is then categorized based on the class of failed component to ensure a significant sample size for each class and accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time to Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. The authors applied these modeling methods to a case study of a crewed mission to the planned SH and produced expected corrective maintenance crew time distributions. The results produced an expected corrective maintenance crew time at over 24 hours per mission, and a maintenance crew time distribution that reflects the importance of planning for sufficient maintenance requirements each mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRS1992

Predit: A temporal predictive framework for scheduling systems

Scheduling can be formalized as a Constraint Satisfaction Problem (CSP). Within this framework activities belonging to a plan are interconnected via temporal constraints that account for slack among them. Temporal representation must include methods for constraints propagation and provide a logic for symbolic and numerical deductions. In this paper we describe a support framework for opportunistic reasoning in constraint directed scheduling. In order to focus the attention of an incremental scheduler on critical problem aspects, some discrete temporal indexes are presented. They are also useful for the prediction of the degree of resources contention. The predictive method expressed through our indexes can be seen as a Knowledge Source for an opportunistic scheduler with a blackboard architecture.

Paolucci, E.↗
NASA NTRS1999

BIO-Plex Information System Concept

This paper describes a suggested design for an integrated information system for the proposed BIO-Plex (Bioregenerative Planetary Life Support Systems Test Complex) at Johnson Space Center (JSC), including distributed control systems, central control, networks, database servers, personal computers and workstations, applications software, and external communications. The system will have an open commercial computing and networking, architecture. The network will provide automatic real-time transfer of information to database server computers which perform data collection and validation. This information system will support integrated, data sharing applications for everything, from system alarms to management summaries. Most existing complex process control systems have information gaps between the different real time subsystems, between these subsystems and central controller, between the central controller and system level planning and analysis application software, and between the system level applications and management overview reporting. An integrated information system is vitally necessary as the basis for the integration of planning, scheduling, modeling, monitoring, and control, which will allow improved monitoring and control based on timely, accurate and complete data. Data describing the system configuration and the real time processes can be collected, checked and reconciled, analyzed and stored in database servers that can be accessed by all applications. The required technology is available. The only opportunity to design a distributed, nonredundant, integrated system is before it is built. Retrofit is extremely difficult and costly.

Jones, Harry↗
NASA NTRS2014

Asteroid Redirect Crewed Mission Space Suit and EVA System Architecture Trade Study

The Asteroid Redirect Crewed Mission (ARCM) requires a Launch/Entry/Abort (LEA) suit capability and short duration Extra Vehicular Activity (EVA) capability for Orion. The EVAs will involve a two-person crew for approximately four hours. Currently, two EVAs are planned with one contingency EVA in reserve. Providing this EVA capability is very challenging due to system level constraints and a new and unknown environment. The goal of the EVA architecture for ARCM is one that builds upon previously developed technologies and lessons learned, and that accomplishes the ARCM mission while providing a stepping stone to future missions and destinations. The primary system level constraints are to 1) minimize system mass and volume and 2) minimize the interfacing impacts to the baseline Orion design. In order to minimize the interfacing impacts and to not perturb the baseline Orion schedule, the concept of adding "kits" to the baseline system is proposed. These kits consist of: an EVA kit (converts LEA suit to EVA suit), EVA Servicing and Recharge Kit (provides suit consumables), the EVA Tools, Translation Aids & Sample Container Kit (the tools and mobility aids to complete the tasks), the EVA Communications Kit (interface between the EVA radio and the MPCV), and the Cabin Repress Kit (represses the MPCV between EVAs). This paper will focus on the trade space, analysis, and testing regarding the space suit (pressure garment and life support system). Historical approaches and lessons learned from all past EVA operations were researched. Previous and current, successfully operated EVA hardware and high technology readiness level (TRL) hardware were evaluated, and a trade study was conducted for all possible pressure garment and life support options. Testing and analysis was conducted and a recommended EVA system architecture was proposed. Pressure garment options that were considered for this mission include the currently in-use ISS EVA Mobility Unit (EMU), all variations of the Advanced Crew Escape Suit (ACES), and the Exploration Z-suit. For this mission, the pressure garment that was selected is the Modified ACES (MACES) with EVA enhancements. Life support options that were considered included short closed-loop umbilicals, long open-loop umbilicals, the currently in-use ISS EMU Portable Life Support System (PLSS), and the currently in development Exploration PLSS. For this mission, the life support option that was selected is the Exploration PLSS. The greatest risk in the proposed architecture is viewed to be the comfort and mobility of the baseline MACES and the delicate balance between adding more mobility features while not compromising landing safety. Feasibility testing was accomplished in low fidelity analogs and in the JSC Neutral Buoyancy Laboratory (NBL) to validate the concept before a final recommendation on the architecture was made. The proposed architecture was found to meet the mission constraints, but much more work is required to determine the details of the required suit upgrades, the integration with the PLSS, and the rest of the tools and equipment required to accomplish the mission. This work and further definition of the remaining kits will be conducted in government fiscal year 14.

Blanco, Raul A.↗
NASA NTRS2018

Improvements to Integrated Tradespace Analysis of Communications Architectures (ITACA) Network Loading Analysis Tool

NASA's SCENIC project aims to simplify and reduce the cost of space mission planning by replicating the analysis capabilities of commercially licensed software which are integrated with relevant analysis parameters specific to SCaN assets and SCaN supported user missions. SCENIC differs from current tools that perform similar analyses in that it 1) does not require any licensing fees, 2) will provide an all-in-one package for various analysis capabilities that normally requires add-ons or multiple tools to complete. As part of SCENIC's capabilities, the ITACA network loading analysis tool will be responsible for assessing the loading on a given network architecture and generating a network service schedule. ITACA will allow users to evaluate the quality of service of a given network architecture and determine whether or not the architecture will satisfy the mission's requirements. ITACA is currently under development, and the following improvements were made during the fall of 2017: optimization of runtime, augmentation of network asset pre-service configuration time, augmentation of Brent's method of root finding, augmentation of network asset FOV restrictions, augmentation of mission lifetimes, and the integration of a SCaN link budget calculation tool. The improvements resulted in (a) 25% reduction in runtime, (b) more accurate contact window predictions when compared to STK(Registered Trademark) contact window predictions, and (c) increased fidelity through the use of specific SCaN asset parameters.

analysis↗
NASA NTRS2023

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗
NASA NTRS2006

The Mars Reconnaissance Orbiter Mission Operations: Architecture and Approach

The Mars Reconnaissance Orbiter (MRO) was launched on August 12, 2005 by an Atlas V launch vehicle from Cape Canaveral Air Force Station. MRO will carry a rich set of science instruments to Mars and provide global, regional survey, and targeted observations. In addition, a set of engineering instruments providing optical navigation, Ka band telecommunication and UHF relay services to future Mars missions are part of the MRO payload. During the mission, the MRO operations teams are presented with two major challenges - unprecedented high data rate and data volumes, and complex science planning and resource sharing. MRO has the capability to communicate with earth at a maximum of six Megabits per second (> 50 times any previous Mars missions). With the current Deep Space Network (DSN) contact schedule of 19 eight-hour tracks per week, the baseline mission plan is for MRO to return 34 Terabits of raw science data during the two year primary science phase. Each of the science instruments has its unique requirements for global mapping, regional survey, and targeted observations. Some instruments prefer nadir-only observations, while others require off-nadir observations (especially for stereo viewing). The requirements from these Mars viewing instruments presented a significant challenge for the operations team to design the complex science planning and resource sharing/allocation process. This paper describes what MRO project is implementing to solve these challenges.

Mars Reconnaissance Orbiter (MRO)↗
NASA NTRS2003

Citizen Explorer: An Earth Observer With New Small Satellite Technology - 1

Citizen Explorer-I (CX-I), designed and built by students at Colorado Space Grant Consortium in Boulder to provide global ozone monitoring, employs a unique mission architecture and several innovative technologies during its mission. The mission design allows K-12 schools around the world to be involved as ground stations available to receive science data and telemetry from CX-I. Another important technology allows the spacecraft to be less reliant on ground operators. Spacecraft Command Language (SCL) allows mission designers to set constraints on the satellite operations. The satellite then automatically adheres to the constraints when the satellite is out of contact with Mission Operations. In addition to SCL, a low level of artificial intelligence will be supplied to the spacecraft through the use of the Automated Scheduling and Planning ENvironment (ASPEN). ASPEN is used to maintain a spacecraft schedule in order to achieve the objectives a mission operator would normally have to complete. Within the communications system of CX-I, internet of CX-I, internet protocols are the main method for communicating with the satellite. As internet protocols have not been widely used in satellite communication, CX-I provides an opportunity to study the effectiveness of using internet protocols over radio links. The Attitude Determination and Control System (ADCS) on CX-I uses a gravity gradient boom as a means of orienting the satellite's science instruments toward nadir. The boom design is unique because it is constructed of tape measure material. These new technologies' effectiveness will be tested for use on future small satellite projects within the space satellite industry.

Allen, Zachary↗
NASA NTRS2010

The Habitat Demonstration Unit System Integration

The Lunar Surface System Habitat Demonstration Unit (HDU) will require a project team to integrate a variety of contributions from National Aeronautics and Space Administration (NASA) centers and potential outside collaborators and poses a challenge in integrating these disparate efforts into a cohesive architecture. To accomplish the development of the first version of the HDU, the Pressurized Excursion Module (PEM), from conception in June 2009 to rollout for operations in July 2010, the HDU project team is using several strategies to mitigate risks and bring the separate efforts together. First, a set of design standards is being developed to define the interfaces between the various systems of PEM and to the payloads, such as the Geology Laboratory, that those systems will support. Scheduled activities such as early fit-checks and the utilization of a habitat avionics test bed prior to equipment installation into HDU PEM are planned to facilitate the integration process. A coordinated effort to establish simplified Computer Aided Design (CAD) standards and the utilization of a modeling and simulation systems will aid in design and integration concept development. Finally, decision processes on the shell development including the assembly sequence and the transportation have been fleshed out early on HDU design to maximize the efficiency of both integration and field operations.

Gill, Tracy R.↗