Autonomous Position, Navigation, and Time (PNT) for Lunar Satellites
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autonomy↗
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Autonomous Lunar Navigation for Rovers and Landers
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Michaela Tarpley↗
Ground Testing of Digital Terrain Models to Prepare for OSIRIS-REx Autonomous Vision Navigation Using Natural Feature Tracking
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C. E. Mario↗
Improved Modeling in a Matlab-Based Navigation System
An innovative approach to autonomous navigation is available for low earth orbit satellites. The system is developed in Matlab and utilizes an Extended Kalman Filter (EKF) to estimate the attitude and trajectory based on spacecraft magnetometer and gyro data. Preliminary tests of the system with real spacecraft data from the Rossi X-Ray Timing Explorer Satellite (RXTE) indicate the existence of unmodeled errors in the magnetometer data. Incorporating into the EKF a statistical model that describes the colored component of the effective measurement of the magnetic field vector could improve the accuracy of the trajectory and attitude estimates and also improve the convergence time. This model is identified as a first order Markov process. With the addition of the model, the EKF attempts to identify the non-white components of the noise allowing for more accurate estimation of the original state vector, i.e. the orbital elements and the attitude. Working in Matlab allows for easy incorporation of new models into the EKF and the resulting navigation system is generic and can easily be applied to future missions resulting in an alternative in onboard or ground-based navigation.
Deutschmann, Julie↗
Systems and Methods for Automated Vessel Navigation Using Sea State Prediction
Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.
Huntsberger, Terrance L.↗
Systems and Methods for Automated Vessel Navigation Using Sea State Prediction
Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.
Huntsberger, Terrance L.↗
Foliage discrimination using a rotating ladar
We present a real time algorithm that detects foliage using range from a rotating laser. Objects not classified as foliage are conservatively labeled as non-driving obstacles. In contrast to related work that uses range statistics to classify objects, we exploit the expected localities and continuities of an obstacle, in both space and time. Also, instead of attempting to find a single accurate discriminating factor for every ladar return, we hypothesize the class of some few returns and then spread the confidence (and classification) to other returns using the locality constraints. The Urbie robot is presently using this algorithm to descriminate drivable grass from obstacles during outdoor autonomous navigation tasks.
Autonomous navigation range robots↗
Evaluation of Relative Navigation Algorithms for Formation-Flying Satellites
Goddard Space Flight Center is currently developing advanced spacecraft systems to provide autonomous navigation and control of formation flyers. This paper discusses autonomous relative navigation performance for formations in eccentric, medium, and high-altitude Earth orbits using Global Positioning System (GPS) Standard Positioning Service (SPS) and intersatellite range measurements. The performance of several candidate relative navigation approaches is evaluated. These analyses indicate that the relative navigation accuracy is primarily a function of the frequency of acquisition and tracking of the GPS signals. A relative navigation position accuracy of 0.5 meters root-mean-square (RMS) can be achieved for formations in medium-attitude eccentric orbits that can continuously track at least one GPS signal. A relative navigation position accuracy of better than 75 meters RMS can be achieved for formations in high-altitude eccentric orbits that have sparse tracking of the GPS signals. The addition of round-trip intersatellite range measurements can significantly improve relative navigation accuracy for formations with sparse tracking of the GPS signals.
Kelbel, David↗
Autonomous landmark based spacecraft navigation system
An autonomous landmark based spacecraft navigation scheme is presented. This new schme involves the following data processing steps: image selection and planning; landmark detection; preliminary image matching; crater matching; database management; and finally landmark based orbit determination.
autonomous↗
Preliminary navigation accuracy analysis for the TDRSS Onboard Navigation System (TONS) experiment on EP/EUVE
A Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (TONS) is currently being developed by NASA to provide a high accuracy autonomous navigation capability for users of TDRSS and its successor, the Advanced TDRSS (ATDRSS). The fully autonomous user onboard navigation system will support orbit determination, time determination, and frequency determination, based on observation of a continuously available, unscheduled navigation beacon signal. A TONS experiment will be performed in conjunction with the Explorer Platform (EP) Extreme Ultraviolet Explorer (EUVE) mission to flight quality TONS Block 1. An overview is presented of TONS and a preliminary analysis of the navigation accuracy anticipated for the TONS experiment. Descriptions of the TONS experiment and the associated navigation objectives, as well as a description of the onboard navigation algorithms, are provided. The accuracy of the selected algorithms is evaluated based on the processing of realistic simulated TDRSS one way forward link Doppler measurements. The analysis process is discussed and the associated navigation accuracy results are presented.
Gramling, C. J.↗
The Deep Space 4/Champollion Comet Rendezvous and Lander Technology Demonstration Mission
The Deep Space 4/Champollion mission is designed to test and validate technologies for landing on and anchoring to small bodies, and sample collection and transfer, in preparation for future sample return missions from comets, asteroids, and satellites. in addition, DS-4 will test technologies for advanced, multi-engine solar electric propulsion (SEP) systems, inflatable-rigidizable solar arrays, autonomous navigation and precision guidance for landing, autonomous hazard detection and avoidance, and advanced integrated avionics and packaging concepts. Deep Space-4/Champollion consists of two spacecraft: an orbiter/carrier vehicle which includes the multi-engine SEP stage, and a lander, called Champollion, which will descend to the surface of the 46P/Tempel 1 cometary nucleus. The spacecraft will launch in April, 2003 and land on the comet in September, 2006 Deep Space 4/Champollion is a joint project between NASA and CNES, the French space agency.
Smythe, William D.↗
A reactive system for open terrain navigation: Performance and limitations
We describe a core system for autonomous navigation in outdoor natural terrain. The system consists of three parts: a perception module which processes range images to identify untraversable regions of the terrain, a local map management module which maintains a representation of the environment in the vicinity of the vehicle, and a planning module which issues commands to the vehicle controller. Our approach is to use the concept of 'early traversability evaluation', and on the use of reactive planning for generating commands to drive the vehicle. We argue that our approach leads to a robust and efficient navigation system. We illustrate our approach by an experiment in which a vehicle travelled autonomously for one kilometer through unmapped cross-country terrain.
Langer, D.↗
NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing
NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.
Jan Stupl↗
NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing
NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.
Jan Stupl↗
Integrating Terrain Maps Into a Reactive Navigation Strategy
An improved method of processing information for autonomous navigation of a robotic vehicle across rough terrain involves the integration of terrain maps into a reactive navigation strategy. Somewhat more precisely, the method involves the incorporation, into navigation logic, of data equivalent to regional traversability maps. The terrain characteristic is mapped using a fuzzy-logic representation of the difficulty of traversing the terrain. The method is robust in that it integrates a global path-planning strategy with sensor-based regional and local navigation strategies to ensure a high probability of success in reaching a destination and avoiding obstacles along the way. The sensor-based strategies use cameras aboard the vehicle to observe the regional terrain, defined as the area of the terrain that covers the immediate vicinity near the vehicle to a specified distance a few meters away.
Howard, Ayanna↗
Closed Loop Terminal Guidance Navigation for a Kinetic Impactor Spacecraft
A kinetic impactor spacecraft is a viable method to deflect an asteroid which poses a threat to the Earth. The technology to perform such a deflection has been demonstrated by the Deep Impact (DI) mission, which successfully collided with comet Tempel 1 in July 2005 using an onboard autonomous navigation system, called AutoNav, for the terminal phase of the mission. In this paper, we evaluate the ability of AutoNav to impact a wide range of scenarios that an deflection mission could encounter, varying parameters such as the approach velocity, phase angle, size of the asteroid, and the determination of spacecraft attitude. Using realistic Monte Carlo simulations, we tabulated the probability of success of the deflection as a function of these parameters, and the highest sensitivity to be due the spacecraft attitude determination mode. In addition, we also specifically analyzed the impact probability for a proposed mission which would send an impactor to the asteroid 1999RQ36. We conclude with some recommendations for future work.
closed loop guidance↗
On-board landmark navigation and attitude reference parallel processor system
An approach to autonomous navigation and attitude reference for earth observing spacecraft is described along with the landmark identification technique based on a sequential similarity detection algorithm (SSDA). Laboratory experiments undertaken to determine if better than one pixel accuracy in registration can be achieved consistent with onboard processor timing and capacity constraints are included. The SSDA is implemented using a multi-microprocessor system including synchronization logic and chip library. The data is processed in parallel stages, effectively reducing the time to match the small known image within a larger image as seen by the onboard image system. Shared memory is incorporated in the system to help communicate intermediate results among microprocessors. The functions include finding mean values and summation of absolute differences over the image search area. The hardware is a low power, compact unit suitable to onboard application with the flexibility to provide for different parameters depending upon the environment.
Gilbert, L. E.↗
Cerebellum Augmented Rover Development
Bio-Inspired Technologies and Systems (BITS) are a very natural result of thinking about Nature's way of solving problems. Knowledge of animal behaviors an be used in developing robotic behaviors intended for planetary exploration. This is the expertise of the JFL BITS Group and has served as a philosophical model for NMSU RioRobolab. Navigation is a vital function for any autonomous system. Systems must have the ability to determine a safe path between their current location and some target location. The MER mission, as well as other JPL rover missions, uses a method known as dead-reckoning to determine position information. Dead-reckoning uses wheel encoders to sense the wheel's rotation. In a sandy environment such as Mars, this method is highly inaccurate because the wheels will slip in the sand. Improving positioning error will allow the speed of an autonomous navigating rover to be greatly increased. Therefore, local navigation based upon landmark tracking is desirable in planetary exploration. The BITS Group is developing navigation technology based upon landmark tracking. Integration of the current rover architecture with a cerebellar neural network tracking algorithm will demonstrate that this approach to navigation is feasible and should be implemented in future rover and spacecraft missions.
King, Matthew↗