Autonomous Navigation for Mars Exploration

The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed

Computationally-Efficient Visual-Inertial Simultaneous Localization and Mapping for Spaceflight Navigation

This thesis represents an investigation into the application to spaceflight of the estimation techniques developed to solve the well-known robotics problem of Simultaneous Localization and Mapping (SLAM). This subject has been thoroughly studied in the context of ground and aerial robotics but its study for use in the spaceflight domain, where the dynamical, measurement, and computational challenges are often very different than in terrestrial applications, is less common. Further, the wide body of extant robotics research into SLAM can be difficult to approach and understand for space navigators with a more classical estimation background because of the differences in terminology and assumptions that roboticists have utilized over time. This work offers an overview of the development of SLAM in robotics and how it has been applied in that field, as well as an accessible approach to the problem for researchers with an aerospace background. This also leads into the development of a novel visual-inertial (VI) SLAM algorithm designed to achieve constant-time exploration and mapping while still integrating the full nonlinear dynamics of the space environment, handling the high update rates of inertial measurement units (IMUs), and incorporating the measurement information produced by a camera sensor. This algorithm is applied to 2D and 3D simulated and real datasets to demonstrate its capability to quickly generate accurate state estimates of both spacecraft and environmental variables.</p

Autonomous Navigation of Distributed Spacecraft using Graph-based SLAM for Proximity Operations in Small Celestial Bodies

Establishment of a sustainable human presence beyond the cislunar space is a major milestone for mankind. Small celestial bodies (SCBs) like asteroids are known to contain valuable natural resources necessary for the development of space assets essential to the accomplishment of this goal. Consequently, future robotic spacecraft missions to SCBs are envisioned with the objective of commercial in-situ resource utilization (ISRU). In mission design, there is also an increasing interest in the utilization of the distributed spacecraft, to benefit from specialization and redundancy. The ability of distributed spacecraft to navigate autonomously in the proximity of a SCB is indispensable for the successful realization of ISRU mission objectives. Quasi-autonomous methods currently used for proximity navigation require extensive ground support for mapping and model development, which can be an impediment for large scale multi-spacecraft ISRU missions in the future. It is prudent to leverage the advances in terrestrial robotic navigation to investigate the development of novel methods for autonomous navigation of spacecraft. The primary objective of the work presented in this thesis is to evaluate the feasibility and investigate the development of methods based on graph-based simultaneous localization and mapping (SLAM), a popular algorithm used in terrestrial autonomous navigation, for the autonomous navigation of distributed spacecraft in the proximity of SCBs. To this end, recent research in graph-based SLAM is extensively studied to identify strategies used to enable multi-agent navigation. The spacecraft navigation requirement is formulated as a graph-based SLAM problem using metric GraphSLAM or topometric graph-based SLAM. Techniques developed based on the identified strategies namely, map merging, inter-spacecraft measurements and relative localization are then applied to this formulation to enable distributed spacecraft navigation. In each case, navigation is formulated in terms of its application to a proximity operation scenario that best suits the multi-agent navigation technique. Several challenges related to the application of graph-based SLAM for spacecraft navigation, such as computational cost and illumination variation are also identified and addressed in the development of these methods. Experiments are performed using simulated models of asteroids and spacecraft dynamics, comparing the estimated states of the spacecraft and landmarks to the assumed true states. The results from the experiments indicate a consistent and robust state determination process, suggesting the suitability of the application of multi-agent navigation techniques to graph-based SLAM for enabling the autonomous navigation of distributed spacecraft near SCBs

Sequential estimation methods for small body optical navigation

As humans explore further into the solar system, small bodies such as asteroids and comets serve as critical stepping-stone destinations. Highly accurate navigation about these small bodies is critical for any future missions, and as a result is listed prominently among NASA's future goals in the NASA Office of Chief Technologist Roadmap. Due to the long communication light-time delays with the Earth, advances in small body navigation may enable missions currently not feasible, as well as significantly reduce dependence on ground resources. Increased operational agility will enable rapid decisions and opportunistic science measurements not possible in previous missions to small bodies. To assist NASA in accomplishing future small body navigation goals, several important advances are made. First, the effectiveness of modern orbit estimation techniques is investigated, with the higher order Additive Divided-Difference sigma point Filter (ADF) implemented and used along with the standard Extended Kalman Filter (EKF) to estimate the spacecraft state from optical small body surface landmark measurements. The ADF performs consistently better than the EKF in the simulations performed, with increasing improvement for higher levels of initial state error and longer intervals between photos of the surface. Second, a new method is created to improve onboard navigation filter performance in diverse and rapidly changing dynamical environments. The approach is to precompute a process noise profile along a reference trajectory using consider covariance analysis tools and filters. When used in an onboard navigation filter, the precomputed process noise allows the filter to account for time- and state-dependent perturbations in the dynamics. The new method also obviates the need for most or all traditional manual tuning of the filter, and provides significantly improved representation of the state uncertainty. Finally, a Simultaneous Localization And Mapping (SLAM) algorithm is employed to estimate the spin state of a tumbling small body (which are expected to be a significant percentage of the small bodies in the solar system), as well as the spacecraft state and surface landmark locations. For the small body characterization phase of the Rosetta mission, the state estimates converge successfully for large initial state errors. The SLAM algorithm remains effective for a range of small body spin states and masses that correspond to expected tumbling small bodies throughout the solar system. The SLAM algorithm is successfully applied to high fidelity independently simulated imagery of a tumbling small body generated by the European Space Agency, and a method for initializing the small body landmark locations is provided.Aerospace Engineerin

Enhancement of Trajectory Determination of Orbiter Spacecraft by Using Pairs of Planetary Optical Images

The subject of the present thesis is about the enhancement of orbiter spacecraft navigation capabilities obtained by the standard radiometric link, taking advantage of an imaging payload and making use of a novel definition of optical measurements. An ESA Mission to Mercury called BepiColombo, was selected as a reference case for this study, and in particular its Mercury Planetary Orbiter (MPO), because of the presence of SIMBIO-SYS, an instrument suite part of the MPO payload, capable of acquiring high resolution images of the surface of Mercury. The use of optical measurements for navigation, can provide complementary informations with respect to Doppler, for enhanced performances or a relaxation of the radio tracking requisites in term of ground station schedule. Classical optical techniques based on centroids, limbs or landmarks, were the base to a novel idea for optical navigation, inspired by concepts of stereoscopic vision. In brief, the relation between two overlapped images acquired by a nadir pointed orbiter spacecraft at different times, was defined, and this information was then formulated into an optical measurement, to be processed by a navigation filter. The formulation of this novel optical observable is presented, moreover the analysis of the possible impact on the mission budget and images scheduling is addressed. Simulations are conducted using an orbit determination software already in use for spacecraft navigation in which the proposed optical measurements were implemented and the final results are given

Spacecraft Relative Navigation Using Random Finite Sets

University of Minnesota M.S. thesis. May 2019. Major: Aerospace Engineering and Mechanics. Advisor: Richard Linares. 1 computer file (PDF); xviii, 69 pages.Future space missions require that spacecraft have onboard capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can have difficulty in these situations when optical sensors are used, diverging due to complications with data association, high measurement uncertainty, and clutter, particularly when detailed a priori maps of the target object or spacecraft do not exist. This thesis demonstrates the feasibility of random finite set (RFS) filters for spacecraft relative navigation and pose estimation. A generalized RPO scenario is formulated as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. An RFS-based filter called the Gaussian Mixture Probability Hypothesis Density (GMPHD) is used. Simulated flash LIDAR measurements are tested, using a GMPHD filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer over time. Results show that an RFS-based filter such as the one used can successfully perform SLAM in a spacecraft relative navigation scenario with no a priori map of the target, and that the formulation behind RFS-based filtering is potentially well suited to spacecraft relative navigation

Space as a New Sphere of Future Information Warfare

Air power has seen constant development from the Wright Flyer’s first flight at Kitty Hawk on December 17, 1903 via the advent of the jet age with the service entry of the Messerschmitt Me 262 in 1942, to today’s multirole fighters (F-35 Joint Strike Fighter) and stealth aircraft (B-2 Spirit multi-role bomber). As a result of this evolution of one hundred years air power has emerged as a central component in power projection. As General William Mitchell said: ”Neither armies nor navies can exist unless the air is controlled over them.” (Mitchell 1925, xv)We have witnessed a corresponding development in space, albeit with a lag of nearly sixty years. The first satellite, the Sputnik, went in orbit on October 4, 1957 and the first manned spaceflight was accomplished on April 12, 1961 (by Yuri Gagarin). July 20, 1969 saw the first landing of man on the moon by Neil Armstrong; the first Space Shuttle launch was on April 12, 1981; and the International Space Station (ISS) has remained manned since November 2, 2000. Since 1961, more than 400 men and women have visited the realm of space. General Tommy Franks said:”The pieces of this operation (Iraqi Freedom) which have been successful would not have been so without space-based assets … it’s just simply a fact.”A major ingredient of success in modern warfare is the capability to collect and analyze information and then use it for the execution of command and control. Intelligence, surveillance, command and control, positioning, and targeting systems along with increasingly technical fire systems will have a key role in this area. Deliberate information warfare operations are conducted during times of crisis and war. They are planned based on of information obtained from intelligence and surveillance assets. The aim of the attacker in information operations is to produce a desired effect on targets by means of psychological warfare such as dissemination of information and other psychological operations; by using network attacks and deception along with other forms of information systems warfare; and by employing electronic warfare assets for jamming, and weapons to suppress the enemy’s intelligence, surveillance, and command and control systems.Space, the electromagnetic spectrum, virtual networks, the psychological domain, and media will occupy central roles in any future information warfare, and all these can be used in both defensive and offensive modes. The foregoing sums up as a concept of global information warfare. We already have space-based C4ISR, targeting, and positioning systems. The successful execution of operations in future wars depends on the gaining and maintaining of space supremacy. Space is in the process of becoming a new dimension in information warfare

Lunar Crater Identification in Digital Images

It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images

Multitarget tracking and terrain-aided navigation using square-root consider filters

Filtering is a term used to describe methods that estimate the values of partially observed states, such as the position, velocity, and attitude of a vehicle, using current observations that are corrupted due to various sources, such as measurement noise, transmission dropouts, and spurious information. The study of filtering has been an active focus of research for decades, and the resulting filters have been the cornerstone of many of humankind\u27s greatest technological achievements. However, these achievements are enabled principally by the use of specialized techniques that seek to, in some way, combat the negative impacts that processor roundoff and truncation error have on filtering. Two of these specialized techniques are known as square-root filters and consider filters. The former alleviates the fragility induced from estimating error covariance matrices by, instead, managing a factorized representation of that matrix, known as a square-root factor. The latter chooses to account for the statistical impacts a troublesome system parameter has on the overall state estimate without directly estimating it, and the result is a substantial reduction in numerical sensitivity to errors in that parameter. While both of these techniques have found widespread use in practical application, they have never been unified in a common square-root consider framework. Furthermore, consider filters are historically rooted to standard, vector-valued estimation techniques, and they have yet to be generalized to the emerging, set-valued estimation tools for multitarget tracking. In this dissertation, formulae for the square-root consider filter are derived, and the result is extended to finite set statistics-based multitarget tracking tools. These results are used to propose a terrain-aided navigation concept wherein data regarding a vehicle\u27s environment is used to improve its state estimate, and square-root consider techniques provide the numerical stability necessary for an onboard navigation application. The newly developed square-root consider techniques are shown to be much more stable than standard formulations, and the terrain-aided navigation concept is applied to a lunar landing scenario to illustrate its applicability to navigating in challenging environments --Abstract, page iii

Navigation for UAVs using Signals of Opportunity

The reliance of Unmanned Aerial Vehicles (UAVs) on Global Navigation Satellite System (GNSS) for autonomous operation represents a significant vulnerability to their reliable and secure operation due to signal interference, both incidental (e.g. terrain shadowing, ionospheric scintillation) and malicious (e.g. jamming, spoofing). An accurate and reliable alternative UAV navigation system is proposed that exploits Signals of Opportunity (SOP) thus offering superior signal strength and spatial diversity compared to satellite signals. Given prior knowledge of the transmitter's position and signal characteristics, the proposed technique utilizes triangulation to estimate the receiver's position. Dual antenna interferometry provides the received signals' Angle of Arrival (AoA) required for triangulation. Reliance on precise knowledge of the antenna system's orientation is removed by combining AoAs from different transmitters to obtain a differential Angles of Arrival (dAoAs). Analysis, simulation, and ground-based experimental techniques are used to characterize system performance; a path to miniaturized system integration is also presented. Results from these ground-based experiments show that when the received signal-to-noise ratio (SNR) is above about 45 dB (typically in within 30 km of the transmitters), the proposed method estimates the receiver's position uncertainty range from less than 20 m to about 60 m with an update rate of 10 Hz