ALBERT

Description: This paper presents a decentralized observer with a consensus filter for the state observation of a discrete-time linear distributed systems. In this setup, each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors' estimates. We assume that the communication graph is connected for all times as well as the sensing graph. It is proven that the state estimates of the proposed observer asymptotically converge to the actual plant states under arbitrarily changing, but connected, communication and sensing topologies. As a byproduct of this research, we also obtained a result on the location of eigenvalues, the spectrum, of the Laplacian for a family of graphs with self-loops.

Description: No abstract available

Description: The Powered Descent Guidance (PDG) algorithm and software for generating Mars pinpoint or precision landing guidance profiles has been enhanced to incorporate thrust-pointing constraints. Pointing constraints would typically be needed for onboard sensor and navigation systems that have specific field-of-view requirements to generate valid ground proximity and terrain-relative state measurements. The original PDG algorithm was designed to enforce both control and state constraints, including maximum and minimum thrust bounds, avoidance of the ground or descent within a glide slope cone, and maximum speed limits. The thrust-bound and thrust-pointing constraints within PDG are non-convex, which in general requires nonlinear optimization methods to generate solutions. The short duration of Mars powered descent requires guaranteed PDG convergence to a solution within a finite time; however, nonlinear optimization methods have no guarantees of convergence to the global optimal or convergence within finite computation time. A lossless convexification developed for the original PDG algorithm relaxed the non-convex thrust bound constraints. This relaxation was theoretically proven to provide valid and optimal solutions for the original, non-convex problem within a convex framework. As with the thrust bound constraint, a relaxation of the thrust-pointing constraint also provides a lossless convexification that ensures the enhanced relaxed PDG algorithm remains convex and retains validity for the original nonconvex problem. The enhanced PDG algorithm provides guidance profiles for pinpoint and precision landing that minimize fuel usage, minimize landing error to the target, and ensure satisfaction of all position and control constraints, including thrust bounds and now thrust-pointing constraints.

Description: A smart-divert capability has been added into the Powered Descent Guidance (PDG) software originally developed for Mars pinpoint and precision landing. The smart-divert algorithm accounts for the landing dispersions of the entry backshell, which separates from the lander vehicle at the end of the parachute descent phase and prior to powered descent. The smart-divert PDG algorithm utilizes the onboard fuel and vehicle thrust vectoring to mitigate landing error in an intelligent way: ensuring that the lander touches down with minimum- fuel usage at the minimum distance from the desired landing location that also avoids impact by the descending backshell. The smart-divert PDG software implements a computationally efficient, convex formulation of the powered-descent guidance problem to provide pinpoint or precision-landing guidance solutions that are fuel-optimal and satisfy physical thrust bound and pointing constraints, as well as position and speed constraints. The initial smart-divert implementation enforced a lateral-divert corridor parallel to the ground velocity vector; this was based on guidance requirements for MSL (Mars Science Laboratory) landings. This initial method was overly conservative since the divert corridor was infinite in the down-range direction despite the backshell landing inside a calculable dispersion ellipse. Basing the divert constraint instead on a local tangent to the backshell dispersion ellipse in the direction of the desired landing site provides a far less conservative constraint. The resulting enhanced smart-divert PDG algorithm avoids impact with the descending backshell and has reduced conservatism.

Description: G-View is a 3D visualization tool for supporting spacecraft guidance, navigation, and control (GN&C) simulations relevant to small-body exploration and sampling (see figure). The tool is developed in MATLAB using Virtual Reality Toolbox and provides users with the ability to visualize the behavior of their simulations, regardless of which programming language (or machine) is used to generate simulation results. The only requirement is that multi-body simulation data is generated and placed in the proper format before applying G-View.

Description: A contact force model was developed for use in touch and go (TAG) surface sampling simulations on small celestial bodies such as comets and asteroids. In TAG scenarios, a spacecraft descending toward the surface of a small body comes into contact with the surface for a short duration of time, collects material samples with a sampler device, and then ascends to leave the surface. The surface contact required 6-DOF (degrees of freedom) dynamics models due to coupling of the attitude and translation dynamics during the contact. The model described here is for contact scenarios that utilize a rotating brush wheel sampler (BWS) to collect surface material. The model includes stiffness and damping of the surface material during BWS vertical motion, lateral friction from the BWS dragging across the surface, and lateral shear from the rotating BWS scooping the surface material. This model is useful for any mission to asteroids or comets that incorporates surface sampling operations.

Description: An enhanced algorithm is developed that builds on a previous innovation of fuel-optimal powered-descent guidance (PDG) for planetary pinpoint landing. The PDG problem is to compute constrained, fuel-optimal trajectories to land a craft at a prescribed target on a planetary surface, starting from a parachute cut-off point and using a throttleable descent engine. The previous innovation showed the minimal-fuel PDG problem can be posed as a convex optimization problem, in particular, as a Second-Order Cone Program, which can be solved to global optimality with deterministic convergence properties, and hence is a candidate for onboard implementation. To increase the speed and robustness of this convex PDG algorithm for possible onboard implementation, the following enhancements are incorporated: 1) Fast detection of infeasibility (i.e., control authority is not sufficient for soft-landing) for subsequent fault response. 2) The use of a piecewise-linear control parameterization, providing smooth solution trajectories and increasing computational efficiency. 3) An enhanced line-search algorithm for optimal time-of-flight, providing quicker convergence and bounding the number of path-planning iterations needed. 4) An additional constraint that analytically guarantees inter-sample satisfaction of glide-slope and non-sub-surface flight constraints, allowing larger discretizations and, hence, faster optimization. 5) Explicit incorporation of Mars rotation rate into the trajectory computation for improved targeting accuracy. These enhancements allow faster convergence to the fuel-optimal solution and, more importantly, remove the need for a "human-in-the-loop," as constraints will be satisfied over the entire path-planning interval independent of step-size (as opposed to just at the discrete time points) and infeasible initial conditions are immediately detected. Finally, while the PDG stage is typically only a few minutes, ignoring the rotation rate of Mars can introduce 10s of meters of error. By incorporating it, the enhanced PDG algorithm becomes capable of pinpoint targeting.

Description: The G-TAG software tool was developed under the R&TD on Integrated Autonomous Guidance, Navigation, and Control for Comet Sample Return, and represents a novel, multi-body dynamics simulation software tool for studying TAG sampling. The G-TAG multi-body simulation tool provides a simulation environment in which a Touch-and-Go (TAG) sampling event can be extensively tested. TAG sampling requires the spacecraft to descend to the surface, contact the surface with a sampling collection device, and then to ascend to a safe altitude. The TAG event lasts only a few seconds but is mission-critical with potentially high risk. Consequently, there is a need for the TAG event to be well characterized and studied by simulation and analysis in order for the proposal teams to converge on a reliable spacecraft design. This adaptation of the G-TAG tool was developed to support the Comet Odyssey proposal effort, and is specifically focused to address comet sample return missions. In this application, the spacecraft descends to and samples from the surface of a comet. Performance of the spacecraft during TAG is assessed based on survivability and sample collection performance. For the adaptation of the G-TAG simulation tool to comet scenarios, models are developed that accurately describe the properties of the spacecraft, approach trajectories, and descent velocities, as well as the models of the external forces and torques acting on the spacecraft. The adapted models of the spacecraft, descent profiles, and external sampling forces/torques were more sophisticated and customized for comets than those available in the basic G-TAG simulation tool. Scenarios implemented include the study of variations in requirements, spacecraft design (size, locations, etc. of the spacecraft components), and the environment (surface properties, slope, disturbances, etc.). The simulations, along with their visual representations using G-View, contributed to the Comet Odyssey New Frontiers proposal effort by indicating problems and/or benefits of different approaches and designs.

Description: An algorithm improves the accuracy with which a lander can be delivered to the surface of Mars. The main idea behind this innovation is the use of a lossless convexification, which converts an otherwise non-convex constraint related to thruster throttling to a convex constraint, enabling convex optimization to be used. The convexification leads directly to an algorithm that guarantees finding the global optimum of the original nonconvex optimization problem with a deterministic upper bound on the number of iterations required for convergence. In this innovation, previous work in powered-descent guidance using convex optimization is extended to handle the case where the lander must get as close as possible to the target given the available fuel, but is not required to arrive exactly at the target. The new algorithm calculates the minimum-fuel trajectory to the target, if one exists, and calculates the trajectory that minimizes the distance to the target if no solution to the target exists. This approach poses the problem as two Second-Order Cone Programs, which can be solved to global optimality with deterministic bounds on the number of iterations required.