Astro2020 Project White Paper: The Cosmic Accelerometer

We propose an experiment, the Cosmic Accelerometer, designed to yield velocity precision of $\leq 1$ cm/s with measurement stability over years to decades. The first-phase Cosmic Accelerometer, which is at the scale of the Astro2020 Small programs, will be ideal for precision radial velocity measurements of terrestrial exoplanets in the Habitable Zone of Sun-like stars. At the same time, this experiment will serve as the technical pathfinder and facility core for a second-phase larger facility at the Medium scale, which can provide a significant detection of cosmological redshift drift on a 6-year timescale. This larger facility will naturally provide further detection/study of Earth twin planet systems as part of its external calibration process. This experiment is fundamentally enabled by a novel low-cost telescope technology called PolyOculus, which harnesses recent advances in commercial off the shelf equipment (telescopes, CCD cameras, and control computers) combined with a novel optical architecture to produce telescope collecting areas equivalent to standard telescopes with large mirror diameters. Combining a PolyOculus array with an actively-stabilized high-precision radial velocity spectrograph provides a unique facility with novel calibration features to achieve the performance requirements for the Cosmic Accelerometer

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Manoeuvre Planning Architecture for the Optimisation of Spacecraft Formation Flying Reconfiguration Manoeuvres

Formation flying of multiple spacecraft collaborating toward the same goal is fast becoming a reality for space mission designers. Often the missions require the spacecraft to perform translational manoeuvres relative to each other to achieve some mission objective. These manoeuvres need to be planned to ensure the safety of the spacecraft in the formation and to optimise fuel management throughout the fleet. In addition to these requirements is it desirable for this manoeuvre planning to occur autonomously within the fleet to reduce operations cost and provide greater planning flexibility for the mission. One such mission that would benefit from this type of manoeuvre planning is the European Space Agency’s DARWIN mission, designed to search for extra-solar Earth-like planets using separated spacecraft interferometry. This thesis presents a Manoeuvre Planning Architecture for the DARWIN mission. The design of the Architecture involves identifying and conceptualising all factors affecting the execution of formation flying manoeuvres at the Sun/Earth libration point L2. A systematic trade-off analysis of these factors is performed and results in a modularised Manoeuvre Planning Architecture for the optimisation of formation flying reconfiguration manoeuvres. The Architecture provides a means for DARWIN to autonomously plan manoeuvres during the reconfiguration mode of the mission. The Architecture consists of a Science Operations Module, a Position Assignment Module, a Trajectory Design Module and a Station-keeping Module that represents a multiple multi-variable optimisation approach to the formation flying manoeuvre planning problem. The manoeuvres are planned to incorporate target selection for maximum science returns, collision avoidance, thruster plume avoidance, manoeuvre duration minimisation and manoeuvre fuel management (including fuel consumption minimisation and formation fuel balancing). With many customisable variables the Architecture can be tuned to give the best performance throughout the mission duration. The implementation of the Architecture highlights the importance of planning formation flying reconfiguration manoeuvres. When compared with a benchmark manoeuvre planning strategy the Architecture demonstrates a performance increase of 27% for manoeuvre scheduling and fuel savings of 40% over a fifty target observation tour. The Architecture designed in this thesis contributes to the field of spacecraft formation flying analysis on various levels. First, the manoeuvre planning is designed at the mission level with considerations for mission operations and station-keeping included in the design. Secondly, the requirements analysis and implementation of Science Operation Module represent a unique insight into the complexity of observation scheduling for exo-planet analysis missions and presents a robust method for autonomously optimising that scheduling. Thirdly, in-depth analyses are performed on DARWIN-based modifications of existing manoeuvre optimisation strategies identifying their strengths and weaknesses and ways to improve them. Finally, though not implemented in this thesis, the design of a Station-keeping Module is provided to add station-keeping optimisation functionality to the Architecture

Two-stage time-optimal formation reconfiguration strategy

A time-optimal reconfiguration strategy for formation flying of autonomous acceleration-controlled agents is presented. In the proposed strategy, the agents are moved to a special designated formation in the time interval between the completion of the mission in the current formation and the issuance of the next reconfiguration command. It is shown that the problem of finding the special designated formation which minimizes the expected value of the reconfiguration time is nonconvex. This optimization problem is treated for two cases of constrained acceleration, and constrained acceleration and velocity. It is shown that in both cases, the search space for finding the special designated formation can be reduced to a convex compact set. An alternative search algorithm is presented for the second case, which consists of searching a vicinity of possible formations, and solving a convex nondifferentiable optimization problem. This search algorithm is typically much faster than the one concerning the acceleration constraint only. The effectiveness of the proposed strategy is illustrated by simulation

Optimal control problems solved via swarm intelligence

Questa tesi descrive come risolvere problemi di controllo ottimo tramite swarm in telligence. Grande enfasi viene posta circa la formulazione del problema di controllo ottimo, in particolare riguardo a punti fondamentali come l’identificazione delle incognite, la trascrizione numerica e la scelta del risolutore per la programmazione non lineare. L’algoritmo Particle Swarm Optimization viene preso in considerazione e la maggior parte dei problemi proposti sono risolti utilizzando una formulazione differential flatness. Quando viene usato l’approccio di dinamica inversa, il problema di ottimo relativo ai parametri di trascrizione è risolto assumendo che le traiettorie da identificare siano approssimate con curve B-splines. La tecnica Inverse-dynamics Particle Swarm Optimization, che viene impiegata nella maggior parte delle applicazioni numeriche di questa tesi, è una combinazione del Particle Swarm e della formulazione differential flatness. La tesi investiga anche altre opportunità di risolvere problemi di controllo ottimo tramite swarm intelligence, per esempio usando un approccio di dinamica diretta e imponendo a priori le condizioni necessarie di ottimalitá alla legge di controllo. Per tutti i problemi proposti, i risultati sono analizzati e confrontati con altri lavori in letteratura. Questa tesi mostra quindi the algoritmi metaeuristici possono essere usati per risolvere problemi di controllo ottimo, ma soluzioni ottime o quasi-ottime possono essere ottenute al variare della formulazione del problema.This thesis deals with solving optimal control problems via swarm intelligence. Great emphasis is given to the formulation of the optimal control problem regarding fundamental issues such as unknowns identification, numerical transcription and choice of the nonlinear programming solver. The Particle Swarm Optimization is taken into account, and most of the proposed problems are solved using a differential flatness formulation. When the inverse-dynamics approach is used, the transcribed parameter optimization problem is solved assuming that the unknown trajectories are approximated with B-spline curves. The Inverse-dynamics Particle Swarm Optimization technique, which is employed in the majority of the numerical applications in this work, is a combination of Particle Swarm and differential flatness formulation. This thesis also investigates other opportunities to solve optimal control problems with swarm intelligence, for instance using a direct dynamics approach and imposing a-priori the necessary optimality conditions to the control policy. For all the proposed problems, results are analyzed and compared with other works in the literature. This thesis shows that metaheuristic algorithms can be used to solve optimal control problems, but near-optimal or optimal solutions can be attained depending on the problem formulation

Cooperative intersection control for autonomous vehicles

Self-driving cars crossing road intersection

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Optimal Finite Thrust Guidance Methods for Constrained Satellite Proximity Operations Inspection Maneuvers

Algorithms are developed to find optimal guidance for an inspector satellite operating nearby a resident space object (RSO). For a non-maneuvering RSO, methods are first developed for a satellite subject to maximum slew rates to conduct an initial inspection of an RSO, where the control variables include the throttle level and direction of the thrust. Second, methods are developed to optimally maneuver a satellite with on/off thrusters into a natural motion circumnavigation or teardrop trajectory, subject to lighting and collision constraints. It is shown that for on/off thrusters, a control sequence can be parameterized to a relatively small amount of control variables and the relative states can be analytically propagated as a function of those control variables. For a maneuvering RSO, differential games are formulated and solved for an inspector satellite to achieve multiple inspection goals, such as aligning with the Sun vector or matching the RSO\u27s energy. The developed algorithms lead to fuel and time savings which can increase the mission life and capabilities of inspector satellites and thus improve space situational awareness for the U.S. Air Force

Robust String Stability of Vehicle Platoons with Communication

This work investigates longitudinal spacing policies and vehicular communication strategies that can reduce inter-vehicular spacing between the vehicles of automated highway platoons, in the presence of parasitic actuation lags. Currently employed platooning technologies rely on the vehicle’s onboard sensors for information of the neighboring vehicles, due to this they may require large spacing between the vehicles to ensure string stability in the presence of uncertainties, such as parasitic actuation lags. More precisely, they require that the minimum employable time headway (hmin) must be lower bounded by 2τ₀ for string stability, where τ₀ is the maximum parasitic actuation lag. Recent studies have demonstrated that using vehicular communication one may be able to employ smaller spacing between vehicles while ensuring robustness to parasitic lags. However, precise results on the extent of such reduction are sparse in the literature. In this work, platoon string stability is used as a metric to study controllers that require vehicular communication, and find the amount of reduction in spacing such controllers can offer. First, the effects of multiple vehicle look ahead in vehicle platoons that employ a Constant Spacing Policy (CSP) based controller without lead vehicle information in the presence of parasitic lags is studied and string instability of such platoons is demonstrated. A robustly string stable CSP controller that employs information from the leader and the immediate predecessor is considered to determine an upper bound on the allowable parasitic lag; for this CSP controller, a design procedure for the selection of controller gains for a given parasitic lag is also provided. For a string of vehicles adopting a Constant Time Headway Policy (CTHP), it is demonstrated that the minimum employable time headway can be further decreased via vehicular communication in the following manner: (1) if the position, velocity and acceleration of the immediate predecessor vehicle is used, then the ii minimum employable time headway hmin can be reduced to τ₀; (2) if the position and velocity information of r immediately preceding vehicles is used, then hmin can be reduced to 4τ₀/(1 + r); (3) furthermore, if the acceleration of ‘r’ immediately preceding vehicles is used, then hmin can be reduced to 2τ₀/(1 + r); and (4) if the position, velocity and acceleration of the immediate and the r-th predecessors are used, then hmin = 2τ₀/(1 + r). Note that cases (3) and (4) provide the same lower bound on the minimum employable time headway; however, case (4) requires much less communicated information. Representative numerical simulations that are conducted to corroborate the above results are discussed. Vehicle formations employing ring structured communication strategies are also studied in this work and a combinatorial approach for developing ring graphs for vehicle formations is proposed. Stability properties of the platoons with ring graphs, limitations of using ring graphs in platoons, and methods to overcome such limitations are explored. In addition, with ring communication structure, it is possible to devise simple ways to recon- figure the graph when vehicles are added to or removed from the platoon or formation, which is also discussed in this work. Further, experimental results using mobile robots for platooning and two-dimensional formations using ring graphs are discussed