Automated spacecraft docking is a concept of operations with several important potential applications. One application that has received a great deal of attention recently is that of an automated docking capable unmanned re-supply spacecraft. In addition to being useful for re-supplying orbiting space stations, automated shuttles would also greatly facilitate the manned exploration of nearby space objects, including the Moon, near-Earth asteroids, or Mars. These vehicles would allow for longer duration human missions than otherwise possible and could even accelerate human colonization of other worlds. This thesis develops an optimal docking controller for an automated docking capable spacecraft. An innovative vision-based relative navigation system called VisNav is used to provide real-time relative position and orientation estimates, while a Kalman post-filter generates relative velocity and angular rate estimates from the VisNav output. The controller's performance robustness is evaluated in a closed-loop automated spacecraft docking simulation of a scenario in circular lunar orbit. The simulation uses realistic dynamical models of the two vehicles, both based on the European Automated Transfer Vehicle. A high-fidelity model of the VisNav sensor adds realism to the simulated relative navigation measurements. The docking controller's performance is evaluated in the presence of measurement noise, with the cases of sensor noise only, vehicle mass errors plus sensor noise, errors in vehicle moments of inertia plus sensor noise, initial starting position errors plus sensor noise, and initial relative attitude errors plus sensor noise each being considered. It was found that for the chosen cases and docking scenario, the final controller was robust to both types of mass property modeling errors, as well as both types of initial condition modeling errors, even in the presence of sensor noise. The VisNav system was found to perform satisfactorily in all test cases, with excellent estimate error convergence characteristics for the scenario considered. These results demonstrate preliminary feasibility of the presented docking system, including VisNav, for space-based automated docking applications.

As NASA prepares to return humans to the moon and establish a long-term presence on the surface, technologies must be developed to access previously unvisited terrain regardless of the condition. Among these technologies is a guidance, navigation and control (GNC) system capable of safely and precisely delivering a spacecraft, whether manned or robotic, to a predetermined landing area. This thesis presents detailed research of both terrain-relative navigation using a terrain-scanning instrument and beacon-relative radiometric navigation using beacons in lunar orbit or on the surface of the moon. The models for these sensors are developed along with a baseline sensor suite that includes an altimeter, IMU, velocimeter, and star camera. Linear covariance analysis is used to rapidly perform the trade studies relevant to this problem and to provide the navigation performance data necessary to determine which navigation method is best suited to support a 100 m 3-σ navigation requirement for landing anytime and anywhere on the moon.

Observing the relative state of two space vehicles has been an active field of research since the earliest attempts at space rendezvous and docking during the 1960's. Several techniques have successfully been employed by several space agencies and the importance of these systems has been repeatedly demonstrated during the on-orbit assembly and continuous re-supply of the International Space Station. More recent efforts are focused on technologies that can enable fully automated navigation and control of space vehicles. Technologies which have previously been investigated or are actively researched include Video Guidance Systems (VGS), Light Detection and Ranging (LIDAR), RADAR, Differential GPS (DGPS) and Visual Navigation Systems. The proposed system leverages the theoretical foundation which has been advanced in the development of VisNav, invented at Texas A & M University, and the miniaturized commercially available Northstar sensor from Evolution Robotics. The dissertation first surveys contemporary technology, followed by an analytical investigation of the coarse sun sensor and errors associated with utilizing it in the near-field. Next, the commercial Northstar sensor is investigated, utilizing fundamentals to generate a theoretical model of its behavior, followed by the development of an experiment for the purpose of investigating and characterizing the sensor's performance. Experimental results are then presented and compared with a numerical simulation of a single-sensor system performance. A case study evaluating a two sensor implementation is presented evaluating the proposed system's performance in a multisensor configuration. The initial theoretical analysis relied on use of the cosine model, which proved inadequate in fully capturing the response of the coarse sun sensor. Fresenel effects were identified as a significant source of unmodeled sensor behavior and subsequently incorporated into the model. Additionally, near-field effects were studied and modeled. The near-field effects of significance include: unequal incidence angle, unequal incidence power, and non-uniform radiated power. It was found that the sensor displayed inherent instabilities in the 0.3 degree range. However, it was also shown that the sensor could be calibrated to this level. Methods for accomplishing calibration of the sensor in the near-field were introduced and feasibility of achieving better than 1 cm and 1 degree relative position and attitude accuracy in close proximity, even on a small satellite platform, was determined.

This thesis considers the Perspective-N-Point (PNP) problem with orthogonal target geometry, as seen in the problem of cubesat relative navigation. Cubesats are small spacecraft often developed for research purposes and to perform missions in space at low cost. Sensor systems for cubesats have been designed that, by providing vector (equivalently line-of-sight, angle, and image plane) measurements, equate relative navigation to a PNP problem. Much study has been done on this problem, but little of it has considered the case where target geometry is known in advance, as is the case with cooperating cubesats. A typical constraint for cubesats, as well as other PNP applications, is processing resources. Therefore, we considered the ability to reduce processing burden of the PNP solution by taking advantage of the known target geometry. We did this by considering a specific P3P solver and a specific point-cloud correspondence (PCC) solver for disambiguating/improving the estimate, and modifying them both to take into account a known orthogonal geometry. The P3P solver was the Kneip solver, and the point-cloud-correspondence solver was the Optimal Linear Attitude Estimator (OLAE). We were able to achieve over 40% reduction in the computational time of the P3P solver, and around 10% for the PCC solver, vs. the unmodified solvers acting on the same problems. It is possible that the Kneip P3P solver was particularly well suited to this approach. Nevertheless, these findings suggest similar investigation may be worthwhile for other PNP solvers, if (1) processing resources are scarce, and (2) target geometry can be known in advance.

A fundamental problem in spacecraft proximity operations is the determination of the 6 degree of freedom relative navigation solution between the observer reference frame and a reference frame tied to a proximal body. For the most unconstrained case, the proximal body may be uncontrolled, and the observer spacecraft has no a priori information on the body. A spacecraft in this scenario must simultaneously map the generally poorly known body being observed, and safely navigate relative to it. Simultaneous localization and mapping(SLAM)is a difficult problem which has been the focus of research in recent years. The most promising approaches extract local features in 2D or 3D measurements and track them in subsequent observations by means of matching a descriptor. These methods exist for both active sensors such as Light Detection and Ranging(LIDAR) or laser RADAR(LADAR), and passive sensors such as CCD and CMOS camera systems. This dissertation presents a method for fusing time of flight(ToF) range data inherent to scanning LIDAR systems with the passive light field measurements of optical systems, extracting features which exploit information from each sensor, and solving the unique SLAM problem inherent to spacecraft proximity operations. Scale Space analysis is extended to unstructured 3D point clouds by means of an approximation to the Laplace Beltrami operator which computes the scale space on a manifold embedded in 3D object space using Gaussian convolutions based on a geodesic distance weighting. The construction of the scale space is shown to be equivalent to both the application of the diffusion equation to the surface data, as well as the surface evolution process which results from mean curvature flow. Geometric features are localized in regions of high spatial curvature or large diffusion displacements at multiple scales. The extracted interest points are associated with a local multi-field descriptor constructed from measured data in the object space. Defining features in object space instead of image space is shown to bean important step making the simultaneous consideration of co-registered texture and the associated geometry possible. These descriptors known as Multi-Field Diffusion Flow Signatures encode the shape, and multi-texture information of local neighborhoods in textured range data. Multi-Field Diffusion Flow Signatures display utility in difficult space scenarios including high contrast and saturating lighting conditions, bland and repeating textures, as well as non-Lambertian surfaces. The effectiveness and utility of Multi-Field Multi-Scale(MFMS) Features described by Multi-Field Diffusion Flow Signatures is evaluated using real data from proximity operation experiments performed at the Land Air and Space Robotics(LASR) Laboratory at Texas A&M University.

The problem of relative spacecraft motion estimation is considered with application to various reference and relative orbits. Mean circular and elliptic orbits are analyzed, with relative orbits ranging in size from 1 km to 10 km. Estimators are built for three propagation models: (i) Gim-Alfriend State Transition Matrix, (ii) the J2-Linearized Equations of Motion for Circular Orbits, and (iii) the Clohessy-Wiltshire Equations of Motion. Two alternative models were developed in an attempt to ac- count for unmodeled nonlinearities: (i) Biased Clohessy-Whiltshire Equations, and (ii) J2 -Linearized State Transition Matrix. Two estimation techniques are presented in an attempt to explore and determine which propagation model minimizes the error residual: the linear Kalman filter is presented under the assumption of vector based, GPS-type measurements; the extended Kalman filter is analyzed assuming angle-range, optical-type measurements. Sampling time is varied to look at the effect of measurement frequency. It is assumed that the orbit of one of the satellites, the chief, is known reasonably well. This work showed that the error residuals from the state estimates were minimized when the propagation technique utilized was the Gim-Alfriend State Transition Matrix. This supports conclusions that are obtained outside of the estimation problem. Additionally, the error residuals obtained when the propagation technique was the Clohessy-Wiltshire Equations is comparable to the more complicated models. Unmodeled nonlinearities affect the magnitude of the error residuals. As expected, the Gim-Alfriend STM comes closest to the truth; for smaller eccentricities (0.005), the Clohessy-Wiltshire EOM show minor deviations from the truth. As the eccentricity increases, the linear models begin to diverge greatly from the true response. The additional two models (the biased CW equations, and the linear STM) show decent performance under specific conditions. The former accounts for some of the unaccounted for nonlinearities. The latter exhibits comparable performance to the Gim-Alfrien STM for circular reference orbits. However, in each case, as the nonlinearity of the problem increases, the accuracy of the model decreases.

Angles-only navigation is simple, robust, and well proven in many applications. However, it is sometimes ill-conditioned for orbital rendezvous and proximity operations because, without a direct range measurement, the distance to approaching satellites must be estimated by firing thrusters and observing the change in the target's bearing. Nevertheless, the simplicity of angles-only navigation gives it great appeal. The viability of this technique for relative navigation is examined by building a high-fidelity simulation and evaluating the sensitivity of the system to sensor errors. The relative performances of square-root filtering methods, including Potter, Carlson, and UD factorization filters, are compared to the conventional and Joseph formulations. Filter performance is evaluated during closed-loop "station keeping" operations in simulation.

The proposed thesis of this dissertation has both a practical element and theoretical component which aim to answer key questions related to the use of angles-only navigation for autonomous orbital rendezvous. The first and fundamental principle to this work argues that an angles-only navigation filter can determine the relative position and orientation (pose) between two spacecraft to perform the necessary maneuvers and close proximity operations for autonomous orbital rendezvous. Second, the implementation of angles-only navigation for on-orbit applications is looked upon with skeptical eyes because of its perceived limitation of determining the relative range between two vehicles. This assumed, yet little understood subtlety can be formally characterized with a closed-form analytical observability criteria which specifies the necessary and sufficient conditions for determining the relative position and velocity with only angular measurements. With a mathematical expression of the observability criteria, it can be used to 1) identify the orbital rendezvous trajectories and maneuvers that ensure the relative position and velocity are observable for angles-only navigation, 2) quantify the degree or level of observability and 3) compute optimal maneuvers that maximize observability. In summary, the objective of this dissertation is to provide both a practical and theoretical foundation for the advancement of autonomous orbital rendezvous through the use of angles-only navigation.

In this work, research to develop algorithms for a package exchange maneuver between two quad-rotor aircraft is presented. First, the development of tools used for this research is discussed. Second, a controller is designed that synchronizes the flight paths and motion of two quad-rotor robots. The controller is used to guide a designated follower quad-rotor to follow a leader aircraft’s position and orientation. The follower aircraft is equipped with a simple mechanical manipulator to compensate for limitations in the aircrafts maneuverability. finally, a sensor architecture study for relative navigation of Unmanned Aerial Vehicles (UAV) is presented. The architecture study presents typical navigation solutions, considers each solution’s appropriateness for close-proximity missions, and compares performance.

Autonomous flight in unstructured, confined, and unknown GPS-denied environments is a challenging problem. Solutions could be tremendously beneficial for scenarios that require information about areas that are difficult to access and that present a great amount of risk. The goal of this research is to develop a new framework that enables improved solutions to this problem and to validate the approach with experiments using a hardware prototype. In Chapter 2 we examine the consequences and practical aspects of using an improved dynamic model for multirotor state estimation, using only IMU measurements. The improved model correctly explains the measurements available from the accelerometers on a multirotor. We provide hardware results demonstrating the improved attitude, velocity and even position estimates that can be achieved through the use of this model. We propose a new architecture to simplify some of the challenges that constrain GPS-denied aerial flight in Chapter 3. At the core, the approach combines visual graph-SLAM with a multiplicative extended Kalman filter (MEKF). More importantly, we depart from the common practice of estimating global states and instead keep the position and yaw states of the MEKF relative to the current node in the map. This relative navigation approach provides a tremendous benefit compared to maintaining estimates with respect to a single global coordinate frame. We discuss the architecture of this new system and provide important details for each component. We verify the approach with goal-directed autonomous flight-test results. The MEKF is the basis of the new relative navigation approach and is detailed in Chapter 4. We derive the relative filter and show how the states must be augmented and marginalized each time a new node is declared. The relative estimation approach is verified using hardware flight test results accompanied by comparisons to motion capture truth. Additionally, flight results with estimates in the control loop are provided. We believe that the relative, vision-based framework described in this work is an important step in furthering the capabilities of indoor aerial navigation in confined, unknown environments. Current approaches incur challenging problems by requiring globally referenced states. Utilizinga relative approach allows more flexibility as the critical, real-time processes of localization and control do not depend on computationally-demanding optimization and loop-closure processes.