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NASA NTRS2001

Preliminary Results of NASA's First Autonomous Formation Flying Experiment: Earth Observing-1 (EO-1)

NASA's first autonomous formation flying mission is completing a primary goal of demonstrating an advanced technology called enhanced formation flying. To enable this technology, the Guidance, Navigation, and Control center at the Goddard Space Flight Center has implemented an autonomous universal three-axis formation flying algorithm in executive flight code onboard the New Millennium Program's (NMP) Earth Observing-1 (EO-1) spacecraft. This paper describes the mathematical background of the autonomous formation flying algorithm and the onboard design and presents the preliminary validation results of this unique system. Results from functionality assessment and autonomous maneuver control are presented as comparisons between the onboard EO-1 operational autonomous control system called AutoCon(tm), its ground-based predecessor, and a stand-alone algorithm.

Folta, David↗

NASA NTRS2015

Lidar Sensor Performance in Closed-Loop Flight Testing of the Morpheus Rocket-Propelled Lander to a Lunar-Like Hazard Field

For the first time, a suite of three lidar sensors have been used in flight to scan a lunar-like hazard field, identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, guide the Morpheus autonomous, rocket-propelled, free-flying test bed to a safe landing on the hazard field. The lidar sensors and GN&C system are part of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) project which has been seeking to develop a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The 3-D imaging flash lidar is a second generation, compact, real-time, air-cooled instrument developed from a number of cutting-edge components from industry and NASA and is used as part of the ALHAT Hazard Detection System (HDS) to scan the hazard field and build a 3-D Digital Elevation Map (DEM) in near-real time for identifying safe sites. The flash lidar is capable of identifying a 30 cm hazard from a slant range of 1 km with its 8 cm range precision at 1 sigma. The flash lidar is also used in Hazard Relative Navigation (HRN) to provide position updates down to a 250m slant range to the ALHAT navigation filter as it guides Morpheus to the safe site. The Doppler Lidar system has been developed within NASA to provide velocity measurements with an accuracy of 0.2 cm/sec and range measurements with an accuracy of 17 cm both from a maximum range of 2,200 m to a minimum range of several meters above the ground. The Doppler Lidar's measurements are fed into the ALHAT navigation filter to provide lander guidance to the safe site. The Laser Altimeter, also developed within NASA, provides range measurements with an accuracy of 5 cm from a maximum operational range of 30 km down to 1 m and, being a separate sensor from the flash lidar, can provide range along a separate vector. The Laser Altimeter measurements are also fed into the ALHAT navigation filter to provide lander guidance to the safe site. The flight tests served as the culmination of the TRL 6 journey for the lidar suite and included launch from a pad situated at the NASA-Kennedy Space Center Shuttle Landing Facility (SLF) runway, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range just off the North end of the runway. The tests both confirmed the expected performance and also revealed several challenges present in the flight-like environment which will feed into future TRL advancement of the sensors. The flash lidar identified hazards as small as 30 cm from the maximum slant range of 450 m which Morpheus could provide, however, it was occasionally susceptible to an increase in range noise due to heated air from the Morpheus rocket plume which entered its Field-of-View (FOV). The flash lidar was also susceptible to pre-triggering on dust during the HRN phase which was created during launch and transported by the wind. The Doppler Lidar provided velocity and range measurements to the expected accuracy levels yet it was also susceptible to signal degradation due to air heated by the rocket engine. The Laser Altimeter, operating with a degraded transmitter laser, also showed signal attenuation over a few seconds at a specific phase of the flight due to the heat plume generated by the rocket engine.

Roback, Vincent E.↗

DOE OSTI2025

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS 2 ANE (Integrated Novelty Score−Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less promising by conventional criteria. We validate this approach on a preacquired data set with a known ground truth comprising of image−spectral pairs. We further implement the process on autonomous scanning probe microscopy experiments. INS 2 ANE significantly increases the diversity of explored phenomena in comparison to conventional optimization routines, enhancing the likelihood of discovering previously unobserved phenomena. These results demonstrate the potential for autonomous microscopy experiments to enhance the scientific discovery by navigating complex experimental spaces to uncover novel phenomena.

Materials↗

NASA NTRS2016

3-D Flash Lidar Performance in Flight Testing on the Morpheus Autonomous, Rocket-Propelled Lander to a Lunar-Like Hazard Field

For the first time, a 3-D imaging Flash Lidar instrument has been used in flight to scan a lunar-like hazard field, build a 3-D Digital Elevation Map (DEM), identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, help to guide the Morpheus autonomous, rocket-propelled, free-flying lander to that safe site on the hazard field. The flight tests served as the TRL 6 demo of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) system and included launch from NASA-Kennedy, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range. The ALHAT project developed a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The Flash Lidar is a second generation, compact, real-time, air-cooled instrument. Based upon extensive on-ground characterization at flight ranges, the Flash Lidar was shown to be capable of imaging hazards from a slant range of 1 km with an 8 cm range precision and a range accuracy better than 35 cm, both at 1-delta. The Flash Lidar identified landing hazards as small as 30 cm from the maximum slant range which Morpheus could achieve (450 m); however, under certain wind conditions it was susceptible to scintillation arising from air heated by the rocket engine and to pre-triggering on a dust cloud created during launch and transported down-range by wind.

Roback, Vincent E.↗

NASA NTRS2002

A Multi-Function Guidance, Navigation and Control System for Future Earth and Space Missions

Over the past several years the Guidance, Navigation and Control Center (GNCC) at NASA's Goddard Space Flight Center (GSFC) has actively engaged in the development of advanced GN&C technology to enable future Earth and Space science missions. The Multi-Function GN&C System (MFGS) design presented in this paper represents the successful coalescence of several discrete GNCC hardware and software technology innovations into one single highly integrated, compact, low power and low cost unit that simultaneously provides autonomous real time on-board attitude determination solutions and navigation solutions with accuracies that satisfy many future GSFC mission requirements. The MFGS is intended to operate as a single self-contained multifunction unit combining the functions now typically performed by a number of hardware units on a spacecraft. However, recognizing the need to satisfy a variety of future mission requirements, design provisions have been included to permit the unit to interface with a number of external remotely mounted sensors and actuators such as magnetometers, sun sensors, star cameras, reaction wheels and thrusters. The result is a highly versatile MFGS that can be configured in multiple ways to suit a realm of mission-specific GN&C requirements. It is envisioned that the MFGS will perform a mission enabling role by filling the microsat GN&C technology gap. In addition, GSFC believes that the MFGS could be employed to significantly reduce volume, power and mass requirements on conventional satellites.

Gambino, Joel↗

NASA NTRS1989

Mars Rover and Sample Return Mission design

The current reference Mars Rover and Sample Return mission is described. Technical issues are outlined, including high-resolution image acquisition and reconstruction, approach navigation, ground and flight systems operational complexity, rover autonomy, autonomous rendezvous and docking in Mars orbit, aerocapture and aeromaneuver, estimating the probability of mission success, and end-to-end information system design. Focus is placed on lander hazard identification and avoidance, pinpoint landing guidance and control, Mars ascent vehicle guidance and control, planetary protection and quarantine, sample acquisition and preservation, project management and control, systems requirements and interface control, and costing. In addition, program issues such as international participation, fiscal constraints, and launch-vehicle availability are considered.

Kwok, Johnny H.↗

NASA NTRS2000

The Mathematics of Navigating the Solar System

In navigating spacecraft throughout the solar system, the space navigator relies on three academic disciplines - optimization, estimation, and control - that work on mathematical models of the real world. Thus, the navigator determines the flight path that will consume propellant and other resources in an efficient manner, determines where the craft is and predicts where it will go, and transfers it onto the optimal trajectory that meets operational and mission constraints. Mission requirements, for example, demand that observational measurements be made with sufficient precision that relativity must be modeled in collecting and fitting (the estimation process) the data, and propagating the trajectory. Thousands of parameters are now determined in near real-time to model the gravitational forces acting on a spacecraft in the vicinity of an irregularly shaped body. Completing these tasks requires mathematical models, analyses, and processing techniques. Newton, Gauss, Lambert, Legendre, and others are justly famous for their contributions to the mathematics of these tasks. More recently, graduate students participated in research to update the gravity model of the Saturnian system, including higher order gravity harmonics, tidal effects, and the influence of the rings. This investigation was conducted for the Cassini project to incorporate new trajectory modeling features in the navigation software. The resulting trajectory model will be used in navigating the 4-year tour of the Saturnian satellites. Also, undergraduate students are determining the ephemerides (locations versus time) of asteroids that will be used as reference objects in navigating the New Millennium's Deep Space 1 spacecraft autonomously.

Hintz, Gerald↗

NASA NTRS2012

Intelligent Optical Systems Using Adaptive Optics

Until recently, the phrase adaptive optics generally conjured images of large deformable mirrors being integrated into telescopes to compensate for atmospheric turbulence. However, the development of smaller, cheaper devices has sparked interest for other aerospace and commercial applications. Variable focal length lenses, liquid crystal spatial light modulators, tunable filters, phase compensators, polarization compensation, and deformable mirrors are becoming increasingly useful for other imaging applications including guidance navigation and control (GNC), coronagraphs, foveated imaging, situational awareness, autonomous rendezvous and docking, non-mechanical zoom, phase diversity, and enhanced multi-spectral imaging. The active components presented here allow flexibility in the optical design, increasing performance. In addition, the intelligent optical systems presented offer advantages in size and weight and radiation tolerance.

Clark, Natalie↗

NASA NTRS2017

Automated Re-Entry System using FNPEG

This paper discusses the implementation and simulated performance of the FNPEG (Fully Numerical Predictor-corrector Entry Guidance) algorithm into GNC FSW (Guidance, Navigation, and Control Flight Software) for use in an autonomous re-entry vehicle. A few modifications to FNPEG are discussed that result in computational savings -- a change to the state propagator, and a modification to cross-range lateral logic. Finally, some Monte Carlo results are presented using a representative vehicle in both a high-fidelity 6-DOF (degree-of-freedom) sim as well as in a 3-DOF sim for independent validation.

Johnson, Wyatt R.↗

NASA NTRS1994

Mobile robot exploration and navigation of indoor spaces using sonar and vision

Integration of skills into an autonomous robot that performs a complex task is described. Time constraints prevented complete integration of all the described skills. The biggest problem was tuning the sensor-based region-finding algorithm to the environment involved. Since localization depended on matching regions found with the a priori map, the robot became lost very quickly. If the low level sensing of the world is not working, then high level reasoning or map making will be unsuccessful.

Kortenkamp, David↗

NASA NTRS2021

Part II: FY20 CIF Annual Report - LAPS: Lunar Autonomous Positioning System

This project concerns construction of an orbital and ground resource network that provides Position, Navigation, and Timing (PNT) services for lunar surface operations. While functionally similar to Global Navigation Satellite Systems (GNSS) for Earth, this system will instead build an automated PNT framework utilizing limited infrastructure on board orbiting assets along with a controlled number of highly accurate assets, or anchor nodes. The goal is to use advanced algorithms to autonomously coordinate on demand or as needed asset participation to achieve orbit determination and time synchronization of accuracy sufficient for end-user localization. Other proposed lunar PNT solutions, such as weak signal GPS, will not meet many mission localization requirements without additional user INS augmentation and a dedicated GNSS constellation would require prohibitive infrastructure development. This proposed effort, the Lunar Autonomous Positioning System (LAPS), instead offers a design that could be deployed in the near term and is facilitated by accessible hardware technology.

Kelley Hashemi↗

NASA NTRS2018

Orion Optical Navigation Progress Toward Exploration Mission 1

Optical navigation of human spacecraft was proposed on Gemini and implemented successfully on Apollo as a means of autonomously operating the vehicle in the event of lost communication with controllers on Earth. The Orion emergency return system utilizing optical navigation has matured in design over the last several years, and is currently undergoing the final implementation and test phase in preparation for Exploration Mission 1 (EM-1) in 2019. The software development is past its Critical Design Review, and is progressing through test and certification for human rating. The filter architecture uses a square-root-free UDU covariance factorization. Linear Covariance Analysis (LinCov) was used to analyze the measurement models and the measurement error models on a representative EM-1 trajectory. The Orion EM-1 flight camera was calibrated at the Johnson Space Center (JSC) electro-optics lab. To permanently stake the focal length of the camera a 500 mm focal length refractive collimator was used. Two Engineering Design Unit (EDU) cameras and an EDU star tracker were used for a live-sky test in Denver. In-space imagery with high-fidelity truth metadata is rare so these live-sky tests provide one of the closest real-world analogs to operational use. A hardware-in-the-loop test rig was developed in the Johnson Space Center Electro-Optics Lab to exercise the OpNav system prior to integrated testing on the Orion vehicle. The software is verified with synthetic images. Several hundred off-nominal images are also used to analyze robustness and fault detection in the software. These include effects such as stray light, excess radiation damage, and specular reflections, and are used to help verify the tuning parameters chosen for the algorithms such as earth atmosphere bias, minimum pixel intensity, and star detection thresholds.

Holt, Greg N.↗

NASA NTRS2024

Goddard Enhanced Onboard Navigation System (GEONS) Mathematical Specifications

The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) has developed the capability to provide high-accuracy attitude, orbit, and time autonomously onboard NASA spacecraft. The GSFC Mission Engineering and Systems Analysis Division has implemented NASA-developed navigation algorithms for high-accuracy real-time onboard orbit determination in the Global Positioning System (GPS) Enhanced Orbit Determination (GEODE) flight software. The Goddard Enhanced Onboard Navigation System (GEONS) extends the capabilities of the GEODE flight software to include additional measurement types and additional navigation algorithms.

Anne C. Long↗

NASA NTRS2016

An Inertial Dual-State State Estimator for Precision Planetary Landing with Hazard Detection and Avoidance

The navigation filter architecture successfully deployed on the Morpheus flight vehicle is presented. The filter was developed as a key element of the NASA Autonomous Landing and Hazard Avoidance Technology (ALHAT) project and over the course of 15 free fights was integrated into the Morpheus vehicle, operations, and flight control loop. Flight testing completed by demonstrating autonomous hazard detection and avoidance, integration of an altimeter, surface relative velocity (velocimeter) and hazard relative navigation (HRN) measurements into the onboard dual-state inertial estimator Kalman flter software, and landing within 2 meters of the vertical testbed GPS-based navigation solution at the safe landing site target. Morpheus followed a trajectory that included an ascent phase followed by a partial descent-to-landing, although the proposed filter architecture is applicable to more general planetary precision entry, descent, and landings. The main new contribution is the incorporation of a sophisticated hazard relative navigation sensor-originally intended to locate safe landing sites-into the navigation system and employed as a navigation sensor. The formulation of a dual-state inertial extended Kalman filter was designed to address the precision planetary landing problem when viewed as a rendezvous problem with an intended landing site. For the required precision navigation system that is capable of navigating along a descent-to-landing trajectory to a precise landing, the impact of attitude errors on the translational state estimation are included in a fully integrated navigation structure in which translation state estimation is combined with attitude state estimation. The map tie errors are estimated as part of the process, thereby creating a dual-state filter implementation. Also, the filter is implemented using inertial states rather than states relative to the target. External measurements include altimeter, velocimeter, star camera, terrain relative navigation sensor, and a hazard relative navigation sensor providing information regarding hazards on a map generated on-the-fly.

Bishop, Robert H.↗

NASA NTRS2013

X-Ray Detection and Processing Models for Spacecraft Navigation and Timing

The current primary method of deepspace navigation is the NASA Deep Space Network (DSN). High-performance navigation is achieved using Delta Differential One-Way Range techniques that utilize simultaneous observations from multiple DSN sites, and incorporate observations of quasars near the line-of-sight to a spacecraft in order to improve the range and angle measurement accuracies. Over the past four decades, x-ray astronomers have identified a number of xray pulsars with pulsed emissions having stabilities comparable to atomic clocks. The x-ray pulsar-based navigation and time determination (XNAV) system uses phase measurements from these sources to establish autonomously the position of the detector, and thus the spacecraft, relative to a known reference frame, much as the Global Positioning System (GPS) uses phase measurements from radio signals from several satellites to establish the position of the user relative to an Earth-centered fixed frame of reference. While a GPS receiver uses an antenna to detect the radio signals, XNAV uses a detector array to capture the individual xray photons from the x-ray pulsars. The navigation solution relies on detailed xray source models, signal processing, navigation and timing algorithms, and analytical tools that form the basis of an autonomous XNAV system. Through previous XNAV development efforts, some techniques have been established to utilize a pulsar pulse time-of-arrival (TOA) measurement to correct a position estimate. One well-studied approach, based upon Kalman filter methods, optimally adjusts a dynamic orbit propagation solution based upon the offset in measured and predicted pulse TOA. In this delta position estimator scheme, previously estimated values of spacecraft position and velocity are utilized from an onboard orbit propagator. Using these estimated values, the detected arrival times at the spacecraft of pulses from a pulsar are compared to the predicted arrival times defined by the pulsar s pulse timing model. A discrepancy provides an estimate of the spacecraft position offset, since an error in position will relate to the measured time offset of a pulse along the line of sight to the pulsar. XNAV researchers have been developing additional enhanced approaches to process the photon TOAs to arrive at an estimate of spacecraft position, including those using maximum-likelihood estimation, digital phase locked loops, and "single photon processing" schemes that utilize all available time data associated with each photon. Using pulsars from separate, non-coplanar locations provides range and range-rate measurements in each pulsar s direction. Combining these different pulsar measurements solves for offsets in position and velocity in three dimensions, and provides accurate overall navigation for deep space vehicles.

Sheikh, Suneel↗

DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS2018

NASA SPLICE Project: Development and Testing of Precision Landing GN&C Technologies

NASA's technology advancement needs for entry, descent and landing call for high-precision, high-rate sensors that can improve navigation accuracy and vehicle control performance. Higher landing accuracy is required for any future human lander missions, and likely, for most robotic missions 1,2. Sensors and algorithms that significantly reduce navigation errors and can image the local terrain will enable landing at locations of high scientific interest that would otherwise pose significant risk to the vehicle. The Safe and Precise Landing-Integrated Capabilities Evolution project, or SPLICE, is developing precision landing and hazard avoidance (PL&HA) technologies for NASA and for potential commercial space flight missions. SPLICE technologies include sensors, algorithms, advanced space flight computing capabilities, and simulation tools used to integrate and study guidance, navigation, and control (GN&C) system performance. SPLICE efforts include hardware-in-the-loop (HWIL) simulation testing, ground testing, and flight testing, including reuse of hardware from the CoOperative Blending of Autonomous Landing Technologies (COBALT) suborbital flight-test payload3,4. Two of the precise navigation sensors that are being developed and matured within SPLICE are LiDARs. Since 2006, NASA Langley has been developing a Navigation Doppler LiDAR (NDL) for precise velocity measurements, and SPLICE is building an NDL engineering test unit (ETU) that will be brought up to TRL 6 following environmental and high-speed1,2 testing. NASA Goddard is developing a Hazard Detection LiDAR (HD LiDAR) engineering development unit (EDU) for SPLICE that has relevance to future human and robotic lander missions. The HD LiDAR will be flight test and matured to TRL 5.

Restrepo, Carolina I.↗

NASA NTRS2010

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.↗