Mars Science Lander GN&C Design for Entry, Descent, and Landing
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guidance and navigation control (GNC)↗
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NESC GN&C TDT Workshop on 2D Image Motion Optical Transfer Functions, Pointing Performance Analysis, and Requirements
What You Will Learn: The focus is on payload imaging performance due to pointing motion. Some historical background on pointing performance analysis is given. The Optical Transfer Function (OTF) and Modulation Transfer Function (MTF) are defined. The imaging performance due to pointing motion is measured by image motion optical transfer functions (IM OTF). IM OTFs are defined for displacement, smear, and jitter motions, which are all rigorously defined. Deterministic and Statistical IM OTFs are briefly derived and graphically illustrated and compared. The IM OTFs are parameterized by pointing error metrics(PEM), which are means and covariances of displacement, smear, and jitter. Emphasis is on procedures and algorithms to evaluate the image motion optical transfer functions and pointing error metrics. Three procedures are covered, which depend on whether the pointing error data is from time-domain simulation, frequency-domain analysis, or stochastic modeling. A method to evaluate the relative contribution of disturbance sources and to identify the most significant contributors is presented. The presentation includes pertinent discussion of flexible structures and control-structure interaction. No single book can adequately cover this subject, so a book is not required for the course. A list of selected articles, reports, documents, and books is provided for reference and further study. Mathis kept to the minimum necessary to convey principles; lengthy derivations are left to the reference material. Graphics are used to illustrate concepts. As with any such learning endeavor, the knowledge gained will be retained and strengthened through actual practice.c©2019–2021 Mark E. Pittelkau— 5
NASA Engineering and Safety Center (NESC)↗
Simple Sensitivity Analysis for Orion Guidance Navigation and Control
The performance of Orion flight software, especially its GNC software, is being analyzed by running Monte Carlo simulations of Orion spacecraft flights. The simulated performance is analyzed for conformance with flight requirements, expressed as performance constraints. Flight requirements include guidance (e.g. touchdown distance from target) and control (e.g., control saturation) as well as performance (e.g., heat load constraints). The Monte Carlo simulations disperse hundreds of simulation input variables, for everything from mass properties to date of launch. We describe in this paper a sensitivity analysis tool ("Critical Factors Tool" or CFT) developed to find the input variables or pairs of variables which by themselves significantly influence satisfaction of requirements or significantly affect key performance metrics (e.g., touchdown distance from target). Knowing these factors can inform robustness analysis, can inform where engineering resources are most needed, and could even affect operations. The contributions of this paper include the introduction of novel sensitivity measures, such as estimating success probability, and a technique for determining whether pairs of factors are interacting dependently or independently. The tool found that input variables such as moments, mass, thrust dispersions, and date of launch were found to be significant factors for success of various requirements. Examples are shown in this paper as well as a summary and physics discussion of EFT-1 driving factors that the tool found.
Pressburger, Tom↗
Maneuverable Automated Tethered Space Tug Simulator
This TIP will develop autonomous capabilities for the space tugs in the Flat Floor Robotics Laboratory (FFRL) for use in support of In-Space Servicing, Assembly, and Manufacturing (ISAM) and future vehicles in the FFRL. The space tugs would be housed on larger spacecraft, such as ISS or Gateway, and deployed for various ISAM activities. When these activities are complete, the space tug can be reeled back to its host spacecraft.
Flat Floor Robotics Lab↗
MSFC-TVC | TB-03: Derivation of Thrust Vector Control (TVC) Actuator-Force / Gimbal-Torque Transformation Matrix
Thrust vector control (TVC) systems for rocket engine propulsion traditionally use a simple linear relationship to convert between actuator forces and torques about the engine gimbal’s center-of-rotation (COR). As shown in Equation (1), the torque about the gimbal COR is proportional to the applied actuator force and the TVC moment arm (MA)—the perpendicular distance between the TVC actuator’s line-of-action (LOA) and the engine gimbal’s COR. While this fundamental relationship remains valid and accurate in a two-dimensional (2D), one-degree-of-freedom (1-DOF) context—particularly in its non-linear formulation as described in the ER63 Technical Bulletin TB-02 (Derivation of Thrust Vector Control (TVC) Engine-Gimbal / Actuator Moment-Arm Geometry)—it becomes limited when extended to three-dimensional (3D), two-degree-of-freedom (2-DOF) analyses. In 3D, out-of-plane angular displacements can arise, causing the gimbaled engine plane-of-motion to become non-coplanar with the respective actuator plane-of-motion. Such misalignments occur due to the rod-end (RE) and/or tail-stock (TE) mounting geometry of individual TVC actuators. These geometric complexities lead to inaccuracies in calculating engine torques and corresponding actuator forces if only the traditional TVC MA relationship is employed. For higher gimbal angular displacements (e.g., greater than 5 degrees), these inaccuracies become more pronounced, necessitating a more robust mathematical framework. This document presents a comprehensive 3D (2-DOF) derivation of a transformation matrix that accurately converts between gimbaled engine torques and TVC actuator forces. By incorporating the necessary geometric and rotational considerations, this new approach corrects the limitations of the traditional TVC MA method. Subsequent sections compare the newly formulated approach to the traditional method, demonstrating its enhanced accuracy and reliability for a broad range of gimbaled engine conditions.
Thrust Vector Control↗
Autonomous GN and C for Spacecraft Exploration of Comets and Asteroids
A spacecraft guidance, navigation, and control (GN&C) system is needed to enable a spacecraft to descend to a surface, take a sample using a touch-and-go (TAG) sampling approach, and then safely ascend. At the time of this reporting, a flyable GN&C system that can accomplish these goals is beyond state of the art. This article describes AutoGNC, which is a GN&C system capable of addressing these goals, which has recently been developed and demonstrated to a maturity TRL-5-plus. The AutoGNC solution matures and integrates two previously existing JPL capabilities into a single unified GN&C system. The two capabilities are AutoNAV and GREX. AutoNAV is JPL s current flight navigation system, and is fairly mature with respect to flybys and rendezvous with small bodies, but is lacking capability for close surface proximity operations, sampling, and contact. G-REX is a suite of low-TRL algorithms and capabilities that enables spacecraft operations in close surface proximity and for performing sampling/contact. The development and integration of AutoNAV and G-REX components into AutoGNC provides a single, unified GN&C capability for addressing the autonomy, close-proximity, and sampling/contact aspects of small-body sample return missions. AutoGNC is an integrated capability comprising elements that were developed separately. The main algorithms and component capabilities that have been matured and integrated are autonomy for near-surface operations, terrain-relative navigation (TRN), real-time image-based feedback guidance and control, and six degrees of freedom (6DOF) control of the TAG sampling event. Autonomy is achieved based on an AutoGNC Executive written in Virtual Machine Language (VML) incorporating high-level control, data management, and fault protection. In descending to the surface, the AutoGNC system uses camera images to determine its position and velocity relative to the terrain. This capability for TRN leverages native capabilities of the original AutoNAV system, but required advancements that integrate the separate capabilities for shape modeling, state estimation, image rendering, defining a database of onboard maps, and performing real-time landmark recognition against the stored maps. The ability to use images to guide the spacecraft requires the capability for image-based feedback control. In Auto- GNC, navigation estimates are fed into an onboard guidance and control system that keeps the spacecraft guided along a desired path, as it descends towards its targeted landing or sampling site. Once near the site, AutoGNC achieves a prescribed guidance condition for TAG sampling (position/orientation, velocity), and a prescribed force profile on the sampling end-effector. A dedicated 6DOF TAG control then implements the ascent burn while recovering from sampling disturbances and induced attitude rates. The control also minimizes structural interactions with flexible solar panels and disallows any part of the spacecraft from making contact with the ground (other than the intended end-effector).
Carson, John M.↗
Evolution of MPCV Service Module Propulsion and GNC Interface Requirements
The Orion Multi-Purpose Crew Vehicle Service Module Propulsion Subsystem provides propulsion for the integrated Crew and Service Module. Updates in the exploration architecture between Constellation and MPCV as well as NASA's partnership with the European Space Agency have resulted in design changes to the SM Propulsion Subsystem and updates to the Propulsion interface requirements with Guidance Navigation and Control. This paper focuses on the Propulsion and GNC interface requirement updates between the Constellation Service Module and the European Service Module and how the requirement updates were driven or supported by architecture updates and the desired use of hardware with heritage to United States and European spacecraft for the Exploration Missions, EM-1 and EM-2.
spacecraft propulsion↗
A Reusable, State-Based Guidance, Control and Navigation Architecture for Future Planetary Missions
Explore the source record for details and available documents.
MPS GNC architecture↗
Challenges and innovations in the guidance, navigation, and control system for entry, descent and landing of NASA's Mars 2020 mission
This paper will focus on the GNC changes implemented to mitigate these challenges and to provide the desired innovations.
Brugarolas, Pau B.↗
Introduction To Space Shuttle Rendezvous Guidance, Navigation, and Control
The Rendezvous Intro continues to be used for training and reference purposes almost 17 years after it was first published. While this document does not address Orion rendezvous, some Orion personnel are using it as a reference for rendezvous GNC and trajectory concepts. This edition contains corrections that have been identified since the 3rd edition was published in 2000. In addition, new material has been added that was developed by the author in the 1990s and early part of the first decade of the 21st century. While some of this material is perhaps too detailed for an introduction, it is of value and the author wanted to make it available to JSC personnel. The sections on Lambert targeting guidance have been significantly expanded.
John L Goodman↗
Flight Testing of Guidance, Navigation and Control Systems on the Mighty Eagle Robotic Lander Testbed
During 2011 a series of progressively more challenging flight tests of the Mighty Eagle autonomous terrestrial lander testbed were conducted primarily to validate the GNC system for a proposed lunar lander. With the successful completion of this GNC validation objective the opportunity existed to utilize the Mighty Eagle as a flying testbed for a variety of technologies. In 2012 an Autonomous Rendezvous and Capture (AR&C) algorithm was implemented in flight software and demonstrated in a series of flight tests. In 2012 a hazard avoidance system was developed and flight tested on the Mighty Eagle. Additionally, GNC algorithms from Moon Express and a MEMs IMU were tested in 2012. All of the testing described herein was above and beyond the original charter for the Mighty Eagle. In addition to being an excellent testbed for a wide variety of systems the Mighty Eagle also provided a great learning opportunity for many engineers and technicians to work a flight program.
Hannan, Mike↗
A Reusable State-Based Guidance, Navigation and Control Architecture for Planetary Missions
NASA's Jet Propulsion Labortory has embarked on the Mission Data System (MDS) project to produce a reusable, integrated flight and ground software architecture.
MDS state GNC↗
AutoGNC Testbed
A simulation testbed architecture was developed and implemented for the integration, test, and development of a TRL-6 flight software set called Auto- GNC. The AutoGNC software will combine the TRL-9 Deep Impact AutoNAV flight software suite, the TRL-9 Virtual Machine Language (VML) executive, and the TRL-3 G-REX guidance, estimation, and control algorithms. The Auto- GNC testbed was architected to provide software interface connections among the AutoNAV and VML flight code written in C, the G-REX algorithms in MATLAB and C, stand-alone image rendering algorithms in C, and other Fortran algorithms, such as the OBIRON landmark tracking suite. The testbed architecture incorporates software components for propagating a high-fidelity truth model of the environment and the spacecraft dynamics, along with the flight software components for onboard guidance, navigation, and control (GN&C). The interface allows for the rapid integration and testing of new algorithms prior to development of the C code for implementation in flight software. This testbed is designed to test autonomous spacecraft proximity operations around small celestial bodies, moons, or other spacecraft. The software is baselined for upcoming comet and asteroid sample return missions. This architecture and testbed will provide a direct improvement upon the onboard flight software utilized for missions such as Deep Impact, Stardust, and Deep Space 1.
Carson, John M., III↗
Simulators for the X-38/CRV Parafoil and Re-Entry Phases
The X38/CRV is unique in several aspects: it does not afford the pilot a forward view through a window and it utilizes a parafoil in the final landing flight phase. As a result of some of these unique attributes the prototype on-board displays will need to provide enhanced situation awareness to aid a crew member in supervising the onboard GNC software to select a landing site, and possible obstacle avoidance during the final flight phase. In the ESA/ESTEC visualization prototype system the scene is visualized using MultiGen OpenFlight models on high-end SGI machines. The Parafoil Guidance Navigation and Control (PGNC) algorithms are being developed as a joint NASA/ESA venture. These algorithms will be used onboard the vehicle to create nominal flight trajectories for the vehicle to follow. These same algorithms are also used as parafoil simulators in our augmented situation awareness system we are developing. The output of the parafoil simulator is displayed on a Windows-based computer running the LandForm FlightVision software. The FlightVision software is used to create the synthetic environment displays and all the necessary HUD symbology. Maps, such as aeronautical charts, as well as satellite imagery are optionally overlaid on the 3D-terrain model to provide additional situation awareness for the crew.
SITUATIONAL AWARENESS↗
NASA Affordable Vehicle Avionics (AVA): Common Modular Avionics System for Nano-Launchers Offering Affordable Access to Space
Small satellites are becoming ever more capable of performing valuable missions for both government and commercial customers. However, currently these satellites can only be launched affordably as secondary payloads. This makes it difficult for the small satellite mission to launch when needed, to the desired orbit, and with acceptable risk. NASA Ames Research Center has developed and tested a prototype low-cost avionics package for space launch vehicles that provides complete GNC functionality in a package smaller than a tissue box with a mass less than 0.84 kg. AVA takes advantage of commercially available, low-cost, mass-produced, miniaturized sensors, filtering their more noisy inertial data with realtime GPS data. The goal of the Advanced Vehicle Avionics project is to produce and flight-verify a common suite of avionics and software that deliver affordable, capable GNC and telemetry avionics with application to multiple nano-launch vehicles at 1 the cost of current state-of-the-art avionics.
Guidance Navigation and Control↗
A GNC Perspective of the Launch and Commissioning of NASA's SMAP (Soil Moisture Active Passive) Spacecraft
The NASA Soil Moisture Active Passive (SMAP) spacecraft was designed to use radar and radiometer measurements to produce global soil moisture measurements every 2-3 days. The SMAP spacecraft is a complicated dual-spinning design with a large 6 meter deployable mesh reflector mounted on a platform that spins at 14.6 rpm while the Guidance Navigation and Control algorithms maintain precise nadir pointing for the de-spun portion of the spacecraft. After launching in early 2015, the Guidance Navigation and Control software and hardware aboard the SMAP spacecraft underwent an intensive spacecraft checkout and commissioning period. This paper describes the activities performed by the Guidance Navigation and Control team to confirm the health and phasing of subsystem hardware and the functionality of the guidance and control modes and algorithms. The operations tasks performed, as well as anomalies that were encountered during the commissioning, are explained and results are summarized.
Brown, Todd S.↗
Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1
The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.
Park, Thomas↗
Navigation and EDL for the Mars Exploration Rovers
A viewgraph presentation on Deep Space Navigation, and Entry, Decent, and Landing (EDL) for Mars Exploration Rovers is shown. The contents include: 1) JPL Spacecraft Operating across the Solar System; 2) 2003 - 2004: The Busiest Period in JPL's History; 3) Deep Space Navigation Will Enable Many of the New NASA Missions; 4) What Exactly is Navigation vs. GNC for Deep Space?; 5) Cruise and Approach: Why is Deep Space Navigation So Difficult?; 6) Project Importance of GNC: Landing Site Selection; 7) Planetary Communications and Tracking; 8) Tracking Data Types; 9) Delta Differential One-Way Range (deltaDOR); 10) All Solutions Leading up to TCM-4 Design; 11) Entry Flight Path Sensitivities; 12) MER Navigation Results; 13) Atmospheric Entry Targeting and Delivery; 14) Landing Ellipse Orientation; 15) MER Landing Site Trade Example; 16) Entry, Descent and Landing: Entry Guidance or What Things Do We NOT do for MER Landings (but we will later...); 17) Entering Martian Space 8:29 p.m. PST (ERT); 18) Entry, Descent and Landing; 19) Entry, Descent and Landing: Terminal Guidance; 20) The Challenge Going from 12,000 mph to Zero in Less Than Six Minutes; 21) Spirit Landing Location; 22) Entry, Descent and Landing: The Future; 23) Powered Descent Time-Line; and 24) Updated Sky Crane Maneuver Description. A short summary is also given on planetary guidance, navigation and control as it pertains to EDL systems
Mars Exploration Rover (MER)↗