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NASA NTRS2021

The Mars 2020 Perseverance Navigation Filter

The Mars 2020 (M2020) Entry Descent and Landing (EDL) system delivered a rover named “Perseverance” at Jezero crater (Mars) on February 18th, 2021. M2020 EDL used a Guidance Navigation and Control (GN&C) system to achieve its landing target objectives. The navigation filter (NavFilter) integrated Descent Inertial Measurement Unit (DIMU) measurements to estimate position and attitude. The NavFilter also used the Terminal Descent Sensor (TDS) to es-timate surface-relative position and velocity corrections. The NavFilter provided all of the dynamic state information to the entry controller, entry guidance, Lander Vision System (LVS) and powered descent guidance and control algo-rithms. The NavFilter for Perseverance is based on Curiosity’s NavFilter with a couple of modifications.

Casoliva, Jordi↗

NASA NTRSDate not supplied

Tall Lunar Towers: Systems Analysis of a Lunar-Surface-Assembled Power, Communication, and Navigation Infrastructure

The National Aeronautics and Space Administration (NASA) intends to develop and maintain a human-lunar presence, requiring infrastructure on the Lunar South Pole. To keep pace with the continued growth in cislunar activity, there is national interest in developing a global lunar infrastructure network for power and Communication, Position, Navigation, and Timing (CPNT). Demonstrating autonomous construction capabilities is an infrastructure-enabling objective. This study explores several architecture trade studies to assess the feasibility of alternative lunar infrastructure concepts. 50 m towers, for instance, enable solar power generation up to 99% of a lunar year in addition to acting as wide-range communication and navigation surface relays. The infrastructure network considered in this study consisted of tower platforms, utilities hosted on the towers, and both crewed and robotic end users. High-level trades were analyzed, such as tower height, location, and distributed vs centralized power grids. Results indicated that even with minimal permanent surface assets (darkness survival power loads on the order of 5 kW or greater), at any of the five locations considered, taller towers resulted in lower infrastructure mass than shorter towers; up to 24% system mass savings were realized by reducing battery mass. Simultaneously, taller towers increased the range of communication coverage; a 50 m tower on the Connecting Ridge had 5 kg more structure and 76% more line-of-sight coverage to a two-meter-tall end user than a 10 m tower with the same location and user height. Leveraging a grid of tall towers for nominal power and communication would not only save up to 2,800 kg in battery mass but would be more maintainable and allow users to take advantage of generational upgrades.

lunar infrastructure↗

NASA NTRSDate not supplied

A Deeper Dive Into the Meaning and Implications of Interoperability for LunaNet Communications and Navigation Services

As part of planning efforts for cislunar exploration and science missions, space agencies have been collaborating with each other to enable communications, networking, Position, Navigation, and Timing (PNT) systems to exchange information and provide services to spacecraft and space systems in transit, in orbit, and on the surface, thus helping each other to achieve their common goals. To achieve commonality and lower cost for mutual benefit, the strategy of interoperability is being adopted to help all the pieces fit together and function smoothly. Interoperability gives cislunar users the ability to operate in a collaborative environment similar to the terrestrial Internet, allowing them to share information, navigate safely despite increasing radio frequency congestion, and follow common processes and procedures for effective joint operations. Unlike prior government-dominated efforts, this ecosystem is expected to include commercial for-profit businesses, non-profit organizations, and academic institutions. Ultimately, the goal is to enable a cislunar ecosystem of service providers and users to contribute and/or utilize infrastructure and capabilities to accomplish mission objectives spanning the full range of human endeavours while supporting a variety of business models. This paper reports on the results of an effort to assist in efforts to frame the development of the international LunaNet architecture by providing a canonical definition of interoperability broad enough to meet these needs, examine architectural and operational implications of the definition, and explore interoperability strategies and tactics for deploying and evolving these services. It describes key systems-of-systems (SoS) (Network-of-Networks) interoperability concepts in the context of sustainment of the ecosystem over time as systems evolve in technologies, standards and Standards Development Organizations, component and subsystem upgrades, and user applications

LunaNet↗

NASA NTRSDate not supplied

Deep Space navigation for the BioSentinel spacecraft science orbit

BioSentinel is an astrobiology small spacecraft mission. The payload consists of two parts, the first has optical and microfluidics sensors, and the second is a Linear Energy Transfer spectrometer that has the objective to measure deep space radiation from events such as coronal mass ejections. The goal of the mission is to observe potential DNA damage due to the radiation in heliocentric space on the living organism Saccharomyces cerevisiae, which is a budding yeast. Two types of this living organism are included in the payload. The first is a natural type that is more radiation tolerant, while the second is a mutant strain that has a deficiency in a gene that allows DNA repair once damage occurs. The impact caused by the radiation on the DNA is compared to an identical sample aboard the International Space Station, as well as another identical sample at a laboratory on the ground. The BioSentinel mission consists of a 6U CubeSat currently ,as of January 2024, active in heliocentric orbit. The spacecraft was launched aboard the first SLS flight as part of the Artemis-I campaign in November 2022. After successful deployment from the launch vehicle, it performed a lunar flyby with an altitude of 406 km. The delta-V imparted by the flyby provided the necessary energy to achieve a heliocentric orbit, in an Earth-trailing pattern. The navigation analysis consisted of a Kalman-filter that utilized data from the Deep Space Network and the ESA Estrack network. All those antennas were needed since the Artemis-1 campaign included the deployment of several other cubesats, therefore the scheduling process required more antenna assets than usual due to simultaneous demands from various missions. The processed tracking data was later also refined with a smoother in order to obtain a more accurate solution. The type of tracking data included TCP, Sequential Range, Doppler and Range formats. The solar radiation pressure coefficient, as well as the delta-V from the deployment and the flyby were modeled to obtain suitable solutions that could decrease the position and velocity uncertainties at several steps along the mission concept of operations. The final product each time resulted in updated ephemeris files that were used by the mission and the antenna networks as the mission progressed. Once in the final science orbit, the utilized antennas are only from the DSN network and the data format is bounded to just TCP. Regular orbit determination is performed, every two weeks. The spacecraft is in a nominal well-known orbit, performing regular operations. This paper includes an analysis of the final science orbit, the techniques and procedures utilized to perform orbit determination and a description of the overall navigation campaign produced during the mission and, more specifically, during the final science operations in Deep Space.

BioSentinel↗

NASA NTRSDate not supplied

Navigation for the ACS3 Solar Sail Mission

NASA’s Advanced Composite Solar Sail System (ACS3) mission consists of a spacecraft that plans to be launched no earlier than April 2024. The spacecraft carries an 81 m2 solar sail that can produce effective ΔV to alter the initial 1000 km sun-synchronous orbit. The main objective of the mission is to demonstrate the capabilities of the solar sail to effectively change the semi-major axis of its initial orbit. Various composite materials were used to produce the sail, together with lightweight booms that will deploy from a 12U CubeSat bus. The ACS3 navigation team at NASA Ames Research Center has built a Flight Dynamics System (FDS) to provide mission navigation and to produce regular ephemeris once in orbit. The FDS can compute the orbit transfers that the spacecraft will perform once the sail is deployed. To achieve that, GPS data is obtained from the spacecraft telemetry and then is used with a Kalman filter and a smoother to obtain an orbit determination solution. The outcome of this process reduces the position and velocity uncertainty in a daily cadence. After that, the state vector output is used to feed a propagation model that includes the updated attitude and orbit of the spacecraft at that given moment. The trajectory model considers the updated attitude plan of the spacecraft as well as the environment conditions such as the solar weather to compute the associated drag and solar radiation pressure. This paper explains in detail the implementation of the FDS, as well as the solar sail solar radiation pressure trajectory model. We also present the results of several potential trajectory models under various assumptions of orbit parameters, attitude, environment, and material properties. In addition, we introduce a trajectory model for potential interplanetary use of an equivalent solar sail in the future. The outcome of this process will be critical to achieving the objective of determining effective semi-major axis change produced by the solar sail.

Andres Dono↗

NASA NTRSDate not supplied

Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE): Pathfinder for Artemis Gateway

The Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) mission was developed by NASA in collaboration with Advanced Space, LLC of Westminster, Colorado. This technology demonstration mission serves as a pathfinder for near rectilinear halo orbit (NHRO) operations around the Moon. The NHRO, (Perilune = 3,200 km; Apolune = 70,000 km) is the intended orbit for NASA’s Artemis Gateway, a small, human-tended space station planned for lunar orbit. The CAPSTONE mission will validate simulations and confirm operational planning for Gateway while also validating performance of navigation and stationkeeping requirements for Gateway’s Power and Propulsion Element. Therefore, this mission will provide operational experience to NASA, commercial, and international missions for operations in a demanding orbital regime. This presentation accompanies the paper of the same title.

Elwood F Agasid↗

NASA NTRS2024

Deeper Dive into Interoperability and Its Implications for LunaNet Communications and Navigation Services

The Artemis program being developed by United States’ (US) National Aeronautics and Space Administration (NASA) is developing the capabilities to return humans to the Moon and establish an initial base camp and associated infrastructure with extensive contributions from international and commercial partners. In planning for cislunar exploration and science missions, space agencies are collaborating to enable communications, networking, and Positioning, Navigation, and Timing (PNT) systems—called LunaNet—to exchange information and provide services to cislunar spacecraft and space systems, thus helping each other to achieve their shared goals. To achieve commonality and lower cost for mutual benefit, the strategy of interoperability is being adopted to help fit all the pieces together and function smoothly. Facilitating interoperability should benefit lunar missions by providing the ability to operate in a collaborative environment similar to the terrestrial Internet. Interoperability allows them to share information, navigate safely despite increasing radio frequency congestion, and follow common processes and procedures for effective joint operations. Unlike prior government-dominated efforts, this ecosystem is expected to include and benefit for-profit (commercial) businesses, non-profit organizations, and academic institutions as active stakeholders. Ultimately, the goal is to enable a cislunar ecosystem of service providers and users to contribute to and/or utilize infrastructure and capabilities to achieve mission objectives that span the full range of human endeavors while supporting a variety of business models. This approach enables a Systems of Systems (SoS), such as a Network-of-Networks, to be sustainable in the context of the LunaNet ecosystem as systems evolve over time in technologies, standards, components, and user applications. This paper reports on the results of an effort to help frame the development of the international LunaNet architecture by providing a canonical definition of interoperability broad enough to meet these needs, examining architectural and operational implications of the definition, and exploring interoperability strategies and tactics to deploy and evolve the services proposed for cislunar exploration and science missions.

interoperability↗

NASA NTRSDate not supplied

Co-Optimization of Navigation System Requirements and Trajectory Design Using a Sweeping Gradient Method and Linear Covariance Analysis

We describe the application of a sweeping gradient method for ordinary differential equations with events (SGM) and linear covariance analysis (LinCov) to the co-optimization of navigation system requirement generation and robust trajectory design. SGM is a method for computing the gradient of trajectory analyses defined by performance indices over initial value problems with events with respect to static parameters. LinCov is an analytic technique for predicting stochastic behavior of dynamical systems. By combining SGM and LinCov, it is possible use efficient, off-the-shelf, gradient-based optimizers to solve a combined robust optimal trajectory and navigation system design problem. In this paper, we formulate the required models to apply the combined SGM and LinCov techniques to a Near-Rectilinear Halo Orbit rendezvous approach scenario and show results for several intermediate problems.

Benjamin W L Margolis↗

NASA NTRSDate not supplied

NASA Space Communications and Navigation: One Network Evolution

The NASA Space Communications and Navigation (SCaN) Program is responsible for providing the essential connectivity to robotic and human space explorers. The missions relying on SCaN range from suborbital and balloon missions to those traveling beyond the edge of the solar system. The demands for communications and navigation services enabled by SCaN (and its affiliated partners) are projected to increase and outpace the current network capacity. At the same time, the Agency finds itself surrounded by a burgeoning commercial space marketplace, technological advancement, and other government agencies that share common interests in space resiliency, robustness, and performance. As a result, SCaN has begun pivoting toward commercial services and collaborating with partners to close capacity and capability gaps. Given these growing demands of the Agency there is increasing need for multi-network solutions. Future mission concepts will rely on both government and commercial capabilities, both Near Space Network capacity and Deep Space Network capacity. Integrating these diverse support services together from a technical, programmatic and implementation standpoint will be key to meet the growing needs of the future. To accomplish this, a more substantive shift is required, and SCaN is reshaping itself to be a customer-centric, service-oriented, high-performance leader in the space communications community. This paper outlines the SCaN One Team, One Mission, One Network approach, and provides a vision for future mission community experience that includes streamlined mission commitment interfaces and clear processes, dynamic network scheduling and load balancing, and higher efficiency data transport and delivery through the integration of cloud infrastructure and services.

Near Space Network↗

NASA NTRS2025

Orion Hardware In The Loop OIMU Stimulation Latency Effect on Navigation State Estimation

Because Hardware In The Loop (HITL) testing involves the integration of flight software and flight hardware on the ground, non-flight-like effects may arise. One of these non-flight-like effects includes Inertial Measurement Unit (IMU) stimulation latency that affects the time in which the measurement is received by the Extended Kalman Filter (EKF). This paper seeks to present the extent to which this stimulation latency affects the navigational state estimate from the Orion navigation system for all phases of flight for Artemis II.

Christopher A Ertl↗

NASA NTRSDate not supplied

Orion Hardware In The Loop OIMU Stimulation Latency Effect on Navigation State Estimation

Christopher A Ertl↗

DOE OSTI2020

Autonomous Navigation and Control of UGVs' in Nuclear Power Plants - 20381

The purpose of the husky A200 ground robot is to autonomously navigate through the places where it is very hazardous for human beings to reach and operate, like nuclear power plants, chemical industries. The aim is to navigate the ground robot autonomously with an Arm mounted on the robot along with the different sensors as camera, and Lidar. The autonomous motion of the robot is controlled by the controller which uses path planner for trajectory generation of the robot. The mission planner uses the current position of the husky A200, given the way points of the initial and the destination it would extract a best possible route based on the current events provided using GMapping. The global reference frame is used for planning the way points. Creating the appropriate path and the actions required to follow the path are given by the motion planner. The motion planner depends on the active sensor data such as obstacles, lanes, based on the sensor data feasible path is generated. Feasibility of the path is determined by the dynamics of the husky and a series of points generated with certain velocity and acceleration profile. The controller adjusts the lateral, longitudinal and yaw motion of the husky to command the behaviors. The kinematic model is developed for kinematic motion of the husky and the dynamic model is developed for transient and steady state characteristics. The images and other type of data captured by the camera are processed through the computational framework used to build machine learning models. TensorFlow will be used for deep learning and to identify and classify different objects around the husky. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1962