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The navigation feasibility of directing a lightweight flyby/probe package to Pluto is discussed, including the subtopics of optical navigation, autonomous navigation, and interfacing with the Russian launcher.
This chapter describes fundamental research aimed at achieving such long-term objectives through application of soft computing techniques for safe and reliable autonomous rover navigation.
State of the practice in navigation around small celestial bodies heavily relies on ground sup- port and human skill, in particular, for perception-based operations such as optical navigation and mapping. This leads to longer duration and more complex mission operations and sub- sequently higher cost. Furthermore, it imposes limitations for certain missions such as fast fly-bys or multi-agent operations. In this work, we present an autonomous navigation strat- egy suitable for approaching small unexplored bodies. During the approach, we estimate the body’s physical properties as well as the spacecraft’s relative trajectory and associated un- certainties. The autonomous navigation strategy, which is solely based on optical measure- ments, begins as soon as the body becomes resolved in the navigation camera and terminates at the start of proximity operations, when the spacecraft makes its first trajectory correction to stay in the vicinity of the body. Our strategy uses multiple image-processing algorithms: light-curve analysis for estimating the target body’s rotation rate, Shape-from-Silhouette for reconstructing the 3D shape and estimating its rotation pole, and feature tracking tailored to Small-Body images for estimating relative navigation parameters. We used the Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) developed by the Jet Propulsion Laboratory to evaluate the feasibility of this multi-phase navigation strategy using simulated images of an approach trajectory. We used the Rosetta mission data to generate photorealistic images to characterise the performance of this approach. This work is based on the assumptions that the spacecraft attitude is known, the body is a principal-axis rotator, a-priori estimates of ephemerides and scale are available, and the body is observed from a zero sun phase only during initial approach. Preliminary results show orbit determination performance that is on par with the human navigation from the Rosetta mission; albeit with a 1% bias in spacecraft-target radial distance estimate. The bias error is likely due to the robustness and accuracy of the visual tracking under dynamic lighting conditions and per- spective changes, which decrease accuracy.
An autonomous/adaptive interplanetary navigation architecture employing neural networks and genetic algorithms is being developed that will alert navigators when a shift from optimal to suboptimal filtering occurs and assist in modifying the filter parameters to resume optical tracking.
This paper discusses the potential of using the Global Positioning System (GPS) to provide autonomous navigation capability to NASA satellites in the 1980 era. Some of the driving forces motivating autonomous navigation are presented. These include such factors as advances in attitude control systems, onboard science annotation, and onboard gridding of imaging data. Simulation results which demonstrate baseline orbit determination accuracies using GPS data on Seasat, Landsat-D, and the Solar Maximum Mission are presented. Emphasis is placed on identifying error sources such as GPS time, GPS ephemeris, user timing biases, and user orbit dynamics, and in a parametric sense on evaluating their contribution to the orbit determination accuracies.
The 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 a formation of four eccentric, medium-altitude Earth-orbiting satellites using Global Positioning System (GPS) Standard Positioning Service (SPS) and "GPS-like " intersatellite measurements. The performance of several candidate relative navigation approaches is evaluated. These analyses indicate that an autonomous relative navigation position accuracy of 1meter root-mean-square can be achieved by differencing high-accuracy filtered solutions if only measurements from common GPS space vehicles are used in the independently estimated solutions.
The performance of a new type of autonomous solar navigation system is analyzed in this paper. Such efficient autonomous navigation systems will reduce operation costs and alleviate the Deep Space Network workload in future space missions. The method is demonstrated by applying it to the STEREO mission. Orbit determination is simulated through the use of the mission-defined trajectory profile and solar angular data acquired by the on-board science instruments currently being considered. The study shows that the orbit solution derived by this new type of solar navigation system can satisfy the mission's navigation requirements; the position uncertainties obtained in the simulations are well below the mission allowable values, and are comparable to the results obtained with the conventional Doppler tracking system in some cases.
Recent work on INS integrated motion analysis is described. Results were obtained with a maximally passive system of obstacle detection (OD) for ground-based vehicles and rotorcraft. The OD approach involves motion analysis of imagery acquired by a passive sensor in the course of vehicle travel to generate range measurements to world points within the sensor FOV. INS data and scene analysis results are used to enhance interest point selection, the matching of the interest points, and the subsequent motion-based computations, tracking, and OD. The most important lesson learned from the research described here is that the incorporation of inertial data into the motion analysis program greatly improves the analysis and makes the process more robust.
The NASA Autonomous Guidance, Navigation and Control (GN&C) Bridging program is reviewed to demonstrate the program plan and GN&C systems for the Space Shuttle. The ascent CN&C system is described in terms of elements such as the general-purpose digital computers, sensors for the navigation subsystem, the guidance-system software, and the flight-control subsystem. Balloon-based and lidar wind soundings are used for operations assessment on the day of launch, and the guidance software is based on dedicated units for atmospheric powered flight, vacuum powered flight, and abort-specific situations. Optimization of the flight trajectories is discussed, and flight-control responses are illustrated for wavelengths of 500-6000 m. Alternate sensors are used for load relief, and adaptive GN&C systems based on alternate gain synthesis are used for systems failures.
Dryden Flight Research Center has the responsibility for flight testing of advanced remotely piloted research vehicles (RPRV) to explore highly maneuverable aircraft technology, and to test advanced structural concepts, and related aeronautical technologies which can yield important research results with significant cost benefits. The primary purpose is to provide the preliminary design of an upgraded automatic approach and landing control system and flight director display to improve landing performance and reduce pilot workload. A secondary purpose is to determine the feasibility of an onboard autonomous navigation, orbit, and landing capability for safe vehicle recovery in the event of loss of telemetry uplink communication with the vehicles. The current RPRV approach and landing method, the proposed automatic and manual approach and autoland system, and an autonomous navigation, orbit, and landing system concept which is based on existing operational technology are described.
This paper describes an error analysis of an autonomous navigator using refraction measurements of starlight passing through the upper atmosphere. The analysis is based on a discrete linear Kalman filter. The filter generated steady-state values of navigator performance for a variety of test cases. Results of these simulations show that in low-earth orbit position-error standard deviations of less than 0.100 km may be obtained using only 40 star sightings per orbit.
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman Filter and Global Positioning System (GPS) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. Current techniques of Kalman filtering, however, still rely on manual tuning from analysts, and cannot help in optimizing autonomy without compromising accuracy and performance. This paper presents an approach to produce a high accuracy autonomous navigation system fully integrated with the flight system. The resulting system performs real-time state estimation by using an Extended Kalman Filter (EKF) implemented with high-fidelity state dynamics model, as does the GPS Enhanced Orbit Determination Experiment (GEODE) system developed by the NASA Goddard Space Flight Center. Augmented to the EKF is a sophisticated neural-fuzzy system, which combines the explicit knowledge representation of fuzzy logic with the learning power of neural networks. The fuzzy-neural system performs most of the self-tuning capability and helps the navigation system recover from estimation errors. The core requirement is a method of state estimation that handles uncertainties robustly, capable of identifying estimation problems, flexible enough to make decisions and adjustments to recover from these problems, and compact enough to run on flight hardware. The resulting system can be extended to support geosynchronous spacecraft and high-eccentricity orbits. Mathematical methodology, systems and operations concepts, and implementation of a system prototype are presented in this paper. Results from the use of the prototype to evaluate optimal control algorithms implemented are discussed. Test data and major control issues (e.g., how to define specific roles for fuzzy logic to support the self-learning capability) are also discussed. In addition, architecture of a complete end-to-end candidate flight system that provides navigation with highly autonomous control using data from GPS is presented.
A number of applications of autonomous satellite navigation are discussed. They include the onboard annotation of all experimental data and the onboard correction of imaging data. The accuracy levels required for each function are presented. Two alternative techniques for achieving autonomous satellite navigation are considered. One technique utilizes data from the Global Positioning System (GPS). The other approach utilizes data from a geosynchronous relay satellite system such as the Tracking and Data Relay Satellite System (TDRSS). The TDRSS approach makes use of one-way range and Doppler data along with a time transfer mechanism. Comparative accuracy levels for the onboard determination of position, velocity, and time based upon the GPS and TDRSS approaches are presented.
The success of JPL's AutoNav system at comet Tempel-1 on July 4, 2005, demonstrated the power of autonomous navigation technology for the Deep Impact Mission. This software is being planned for use as the onboard navigation, tracking and rendezvous system for a Mars Sample Return Mission technology demonstration, and several mission proposals are evaluating its use for rendezvous with, and landing on asteroids. Before this however, extensive re-engineering of AutoNav will take place. This paper describes the AutoNav systems-engineering effort in several areas: extending the capabilities, improving operability, utilizing new hardware elements, and demonstrating the new possibilities of AutoNav in simulations.
A Modular Autonomous Robotic System (MARS), consisting of a modular autonomous vehicle control system that can be retrofit on to any vehicle to convert it to autonomous control and support a modular payload for multiple applications is being developed. The MARS design is scalable, reconfigurable, and cost effective due to the use of modern open system architecture design methodologies, including serial control bus technology to simplify system wiring and enhance scalability. The design is augmented with modular, object oriented (C++) software implementing a hierarchy of five levels of control including teleoperated, continuous guidepath following, periodic guidepath following, absolute position autonomous navigation, and relative position autonomous navigation. The present effort is focused on producing a system that is commercially viable for routine autonomous patrolling of known, semistructured environments, like environmental monitoring of chemical and petroleum refineries, exterior physical security and surveillance, perimeter patrolling, and intrafacility transport applications.
Long communication delays and the expense of supporting large-scale, human-managed spacecraft operations are significant impediments to deep-space exploration; however, implementing autonomy on-board the spacecraft can help overcome these hurdles. In this study, we investigate the feasibility of using on-board autonomous optical navigation techniques during approach to and proximity operations around the ice giants (i.e., Uranus and Neptune) while using the planets' satellites as optical beacons. Using beacon visibility and navigation uncertainties as metrics, we show what requirements (e.g., camera properties and beacon uncertainties) are needed to make these autonomously navigated missions feasible.
This paper presents a novel technique for real-time measurement of terrain characteristics and incorporation of this information into the navigation strategy of an autonomous mobile robot.
This slide presentation reviews the missions that will be flown into deep space in the next 25 years, the navigational challenges for these missions, and the strategies that will be used to overcome these challenges. The challenges include: (1) an incresed need for autonomous navigation, (2) an increased use of in-situ and optical navigation, (3) an increased use of low-thrust propulsion, (4) an increased need for higher accuracy in guidance, navigation, and control, and an increased need for integration between flight path and attitude control. The enabling strategies that are planned for use are: (1) Advance Radio-Metric Tracking Capabilities, (2) Expand the Use of Optical Navigation, (3) Develop General-Purpose Autonomous Navigation Capabilities, (4) Improve Frequency and Timing Systems, and (5) Develop In-situ Tracking Infrastructure. Future trends that are being developed are Optical and Autonomous Navigation