The Deep Space Positioning System (DPS) – Navigator Concept for the Lunar Gateway
UNKNOWN
Vaughan, Andrew T.↗
Engineering topics
Riedel, Joseph E.
Publications and source records attributed to Riedel, Joseph E..
The Deep Space Positioning System (DPS) - navigator concept for the Lunar Gateway
For the lunar Gateway, the optical only version is sufficient given the abundance of optical targets in the form of lunar surface landmarks.
Vaughan, Andrew T.↗
Methods for Passive Optical Detection and Relative Navigation for Rendezvous with a Non-Cooperative Object at Mars
Long-range passive optical detection of an orbiting inert sphere by a robotic Mars orbiter is investigated and trades are described in terms of detectability via reflected visible light in the presence of orbit uncertainty, gravitational perturbations, and camera electronics noise. A new approximate equation for signal-tonoise ratio (SNR) is developed to include most relevant camera imperfections, diffraction, and stray light from the Mars limb as relevant to this scenario. Using this method, a notional camera suite is designed to meet detection, navigation, and redundancy requirements for an example mission scenario. Results from a simulation tool demonstrate the long-range initial detection strategy in the presence of perturbations from various sources. Finally, navigation analysis shows that the information gathered using passive optical detection is sufficient to begin orbit matching.
Woolley, Ryan C.↗
The Deep-Space Positioning System Concept: Automating Complex Navigation Operations Beyond the Earth
There is no equivalent of GPS in “deep space,” that is beyond the Earth Moon system (and use of GPS beyond LEO and MEO is already problematic.) NASA has been awarded a patent on the concept of the “Deep-space Positioning System” (DPS) which is a self-contained instrument concept that would use in-situ observations to provide an automated and autonomous means of determining its position in the Solar System, and at least as important, its position relative to a target, whether that target is a planet, asteroid, moon or comet. In addition, the DPS system computes trajectory change parameters, as necessary to keep a spacecraft on a planned trajectory. Implementation options include the use of a radio receiver to receive a frequency beacon from a non-in-situ source for additional navigation information over and above that which can be provided by the cameras that are the principal data source of DPS. This paper discusses the DPS instrument concept, and its potential use in deep space.
Guinn, Joseph R.↗
Deep Space Positioning System
A single, compact, lower power deep space positioning system (DPS) configured to determine a location of a spacecraft anywhere in the solar system, and provide state information relative to Earth, Sun, or any remote object. For example, the DPS includes a first camera and, possibly, a second camera configured to capture a plurality of navigation images to determine a state of a spacecraft in a solar system. The second camera is located behind, or adjacent to, a secondary reflector of a first camera in a body of a telescope.
Vaughan, Andrew T.↗
Radioisotope Power Systems (RPS) for Emerging Power Needs of Deep Space Cubesats, Smallsats and Deployed Payloads
No abstract available
Lee, Young H.↗
Guidance, Navigation, and Control Technology Assessment for Future Planetary Science Missions
Future planetary explorations envisioned by the National Research Council's (NRC's) report titled Vision and Voyages for Planetary Science in the Decade 2013-2022, developed for NASA Science Mission Directorate (SMD) Planetary Science Division (PSD), seek to reach targets of broad scientific interest across the solar system. This goal requires new capabilities such as innovative interplanetary trajectories, precision landing, operation in close proximity to targets, precision pointing, multiple collaborating spacecraft, multiple target tours, and advanced robotic surface exploration. Advancements in Guidance, Navigation, and Control (GN&C) and Mission Design in the areas of software, algorithm development and sensors will be necessary to accomplish these future missions. This paper summarizes the key GN&C and mission design capabilities and technologies needed for future missions pursuing SMD PSD's scientific goals.
position↗
VML 3.0 Reactive Sequencing Objects and Matrix Math Operations for Attitude Profiling
VML (Virtual Machine Language) has been used as the sequencing flight software on over a dozen JPL deep-space missions, most recently flying on GRAIL and JUNO. In conjunction with the NASA SBIR entitled "Reactive Rendezvous and Docking Sequencer", VML version 3.0 has been enhanced to include object-oriented element organization, built-in queuing operations, and sophisticated matrix / vector operations. These improvements allow VML scripts to easily perform much of the work that formerly would have required a great deal of expensive flight software development to realize. Autonomous turning and tracking makes considerable use of new VML features. Profiles generated by flight software are managed using object-oriented VML data constructs executed in discrete time by the VML flight software. VML vector and matrix operations provide the ability to calculate and supply quaternions to the attitude controller flight software which produces torque requests. Using VML-based attitude planning components eliminates flight software development effort, and reduces corresponding costs. In addition, the direct management of the quaternions allows turning and tracking to be tied in with sophisticated high-level VML state machines. These state machines provide autonomous management of spacecraft operations during critical tasks like a hypothetic Mars sample return rendezvous and docking. State machines created for autonomous science observations can also use this sort of attitude planning system, allowing heightened autonomy levels to reduce operations costs. VML state machines cannot be considered merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. The state machine approach enabled by VML 3.0 is progressing toward flight capability with a wide array of applicable mission activities.
VML (Virtual Machine Language)↗
Virtual Machine Language 2.1
VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that signal is raised. The selected signal then causes all identically named transitions in all present state machines to be taken simultaneously. VML 2.1 has relevance to all potential space missions, both manned and unmanned. It was under consideration for use on Orion.
Riedel, Joseph E.↗
Surface Modeling to Support Small-Body Spacecraft Exploration and Proximity Operations
In order to simulate physically plausible surfaces that represent geologically evolved surfaces, demonstrating demanding surface-relative guidance navigation and control (GN&C) actions, such surfaces must be made to mimic the geological processes themselves. A report describes how, using software and algorithms to model body surfaces as a series of digital terrain maps, a series of processes was put in place that evolve the surface from some assumed nominal starting condition. The physical processes modeled in this algorithmic technique include fractal regolith substrate texturing, fractally textured rocks (of empirically derived size and distribution power laws), cratering, and regolith migration under potential energy gradient. Starting with a global model that may be determined observationally or created ad hoc, the surface evolution is begun. First, material of some assumed strength is layered on the global model in a fractally random pattern. Then, rocks are distributed according to power laws measured on the Moon. Cratering then takes place in a temporal fashion, including modeling of ejecta blankets and taking into account the gravity of the object (which determines how much of the ejecta blanket falls back to the surface), and causing the observed phenomena of older craters being progressively buried by the ejecta of earlier impacts. Finally, regolith migration occurs which stratifies finer materials from coarser, as the fine material progressively migrates to regions of lower potential energy.
Riedel, Joseph E.↗
Mars Sample Return - Launch and Detection Strategies for Orbital Rendezvous
This study sets forth conceptual mission design strategies for the ascent and rendezvous phase of the proposed NASA/ESA joint Mars Sample Return Campaign. The current notional mission architecture calls for the launch of an acquisition/cache rover in 2018, an orbiter with an Earth return vehicle in 2022, and a fetch rover and ascent vehicle in 2024. Strategies are presented to launch the sample into a coplanar orbit with the Orbiter which facilitate robust optical detection, orbit determination, and rendezvous. Repeating ground track orbits exist at 457 and 572 km which provide multiple launch opportunities with similar geometries for detection and rendezvous.
Orbiter's trans-Earth injection (TEI):↗
Mars Sample Return: Launch and Detection Strategies for Orbital Rendezvous
This study sets forth conceptual mission design strategies for the ascent and rendezvous phase of the proposed NASA/ESA joint Mars Sample Return Campaign. The current notional mission architecture calls for the launch of an acquisition/ caching rover in 2018, an Earth return orbiter in 2022, and a fetch rover with ascent vehicle in 2024. Strategies are presented to launch the sample into a nearly coplanar orbit with the Orbiter which would facilitate robust optical detection, orbit determination, and rendezvous. Repeating ground track orbits existat 457 and 572 km which would provide multiple launch opportunities with similar geometries for detection and rendezvous.
transfer scenario↗
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.↗
Altair Navigation During Trans-Lunar Cruise, Lunar Orbit, Descent and Landing
The Altair lunar lander navigation system is driven by a set of requirements that not only specify a need to land within 100 m of a designated spot on the Moon, but also be capable of a safe return to an orbiting Orion capsule in the event of loss of Earth ground support. These requirements lead to the need for a robust and capable on-board navigation system that works in conjunction with an Earth ground navigation system that uses primarily ground-based radiometric tracking. The resulting system relies heavily on combining a multiplicity of data types including navigation state updates from the ground based navigation system, passive optical imaging from a gimbaled camera, a stable inertial measurement unit, and a capable radar altimeter and velocimeter. The focus of this paper is on navigation performance during the trans-lunar cruise, lunar orbit, and descent/landing mission phases with the goal of characterizing knowledge and delivery errors to key mission events, bound the statistical delta V costs for executing the mission, as well as the determine the landing dispersions due to navigation. This study examines the nominal performance that can be obtained using the current best estimate of the vehicle, sensor, and environment models. Performance of the system under a variety sensor outages and parametric trades is also examined.
lunar lander↗
Preliminary Design of the Guidance, Navigation, and Control System of the Altair Lunar Lander
Guidance, Navigation, and Control (GN&C) is the measurement and control of spacecraft position, velocity, and attitude in support of mission objectives. This paper provides an overview of a preliminary design of the GN&C system of the Lunar Lander Altair. Key functions performed by the GN&C system in various mission phases will first be described. A set of placeholder GN&C sensors that is needed to support these functions is next described. To meet Crew safety requirements, there must be high degrees of redundancy in the selected sensor configuration. Two sets of thrusters, one on the Ascent Module (AM) and the other on the Descent Module (DM), will be used by the GN&C system. The DM thrusters will be used, among other purposes, to perform course correction burns during the Trans-lunar Coast. The AM thrusters will be used, among other purposes, to perform precise angular and translational controls of the ascent module in order to dock the ascent module with Orion. Navigation is the process of measurement and control of the spacecraft's "state" (both the position and velocity vectors of the spacecraft). Tracking data from the Earth-Based Ground System (tracking antennas) as well as data from onboard optical sensors will be used to estimate the vehicle state. A driving navigation requirement is to land Altair on the Moon with a landing accuracy that is better than 1 km (radial 95%). Preliminary performance of the Altair GN&C design, relative to this and other navigation requirements, will be given. Guidance is the onboard process that uses the estimated state vector, crew inputs, and pre-computed reference trajectories to guide both the rotational and the translational motions of the spacecraft during powered flight phases. Design objectives of reference trajectories for various mission phases vary. For example, the reference trajectory for the descent "approach" phase (the last 3-4 minutes before touchdown) will sacrifice fuel utilization efficiency in order to provide landing site visibility for both the crew and the terrain hazard detection sensor system. One output of Guidance is the steering angle commands sent to the 2 degree-of-freedom (dof) gimbal actuation system of the descent engine. The engine gimbal actuation system is controlled by a Thrust Vector Control algorithm that is designed taking into account the large quantities of sloshing liquids in tanks mounted on Altair. In this early design phase of Altair, the GN&C system is described only briefly in this paper and the emphasis is on the GN&C architecture (that is still evolving). Multiple companion papers will provide details that are related to navigation, optical navigation, guidance, fuel sloshing, rendezvous and docking, machine-pilot interactions, and others. The similarities and differences of GN&C designs for Lunar and Mars landers are briefly compared.
Lee, Allan Y.↗
Optical Navigation Plan and Strategy for the Lunar Lander Altair
This paper reviews the currently planned Altair Optical Navigation (OpNav) system. The discussion includes description of the OpNav camera manifest. The Altair OpNav plan envisions one, OpNav camera assembly, with perhaps a functional backup that includes a wide angle-imager (of 40 deg to 60 deg field of view - FOV), and a narrow angle imager (of 1 to 3 deg FOV) co-mounted on a 2-degree-of-freedom gimbal. Both imagers are assumed to be relatively wide aperture and large dynamic range to provide excellent short-exposure images at mid-latitudes, and adequate images of longer-exposure near the poles. Landmark modeling and tracking methodology is discussed, including the stereophotoclinometry method assumed to be used to obtain high-accuracy terrain maps at lunar landing sites of 1 - 2 m, and 50 - 100 m elsewhere, using the images expected to be obtained from the Lunar Reconnaissance Orbiter (LRO). Characteristics of the OpNav navigation system are discussed and architecture and results from landing simulations presented, showing expected landing accuracies of better than 10m.
TAG↗
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↗