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Daniel Guggenheim School of Aerospace Engineering

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search.filters.applied.f.advisor: Feron, Eric × End date: 2019 × Start date: 2000 ×

Publication Search Results

Now showing 1 - 10 of 22
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    Decentralized allocation of safety-critical applications on parallel computing architecture
    (Georgia Institute of Technology, 2019-08-26) Sutter, Louis
    This work presents a decentralized task allocation algorithm for an abstract parallel computing architecture made of a set of Computational Units connected together, each of them being prone to fail. Such an architecture can represent for example a multi-core processor with each Computational Unit standing for one core. The aim of the algorithm is to find the best mapping between Computational Units and the different applications we want to execute on the architecture, while taking into account faulty Computational Resources and the priority of the applications. The proposed approach consists in formulating the allocation problem as an Integer Linear Program (ILP), that is solved thanks to a state-of-the-art ILP solver. The second main aspect of this work is the decentralization the allocation process, in the sense that no central element decides alone of the allocation for the rest of the network. Redundant copies of the allocation algorithm are executed on the architecture itself, meaning that the copies must reallocate themselves. Then, the proposed allocation process is implemented on an experimental setup reproducing the multi-core architecture that inspired this work. Each core is represented by a Raspberry Pi single board computer. The model is used to demonstrate the capabilities of the proposed allocation process to maintain operation of a physical system in a decentralized way, while individual components fail.
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    Prediction of limit cycle oscillations in piecewise linear systems
    (Georgia Institute of Technology, 2019-04-01) Yoon, Yong Eun
    The exact mathematical model of most of mechanical and/or electrical systems involves the piecewise linear system, which consists of linear parts along with piecewise nonlinearities. Piecewise linear systems can possess a periodic solution called a limit-cycle oscillation (LCO), which can seriously undermine the system performance depending on the amplitude and the frequency of the LCO. Therefore, how to analyze LCO and its parameters of piecewise linear systems is one of the primary concerns for the control and system engineers of the system. This thesis work presents a novel framework to predict and analyze LCO of piecewise linear systems. On top of the well-known piecewise linear analysis we apply the Floquet theory to identify LCO parameters and determine the stability of the LCO. The introduction of Floquet theory to piecewise linear systems is allowed through transforming piecewise nonlinearities to corresponding equivalent analytic functions. In addition, the establishment of switching equation provides another necessary condition to predict LCO parameters. We take an example of a realistic flight control system to demonstrate the effectiveness and efficiency of our framework.
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    Formal verification and validation of convex optimization algorithms for model predictive control
    (Georgia Institute of Technology, 2018-12-13) Cohen, Raphael P.
    The efficiency of modern optimization methods, coupled with increasing computational resources, has led to the possibility of real-time optimization algorithms acting in safety critical roles. However, this cannot happen without addressing proper attention to the soundness of these algorithms. This PhD thesis discusses the formal verification of convex optimization algorithms with a articular emphasis on receding-horizon controllers. Additionally, we demonstrate how theoretical proofs of real-time optimization algorithms can be used to describe functional properties at the code level, thereby making it accessible for the formal methods community. In seeking zero-bug software, we use the Credible Autocoding scheme. We focused our attention on the ellipsoid algorithm solving second-order cone programs (SOCP). In addition to this, we present a floating-point analysis of the algorithm and give a framework to numerically validate the method.
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    Adaptive filtering for vision-aided inertial navigation
    (Georgia Institute of Technology, 2018-11-09) Lee, Kyuman
    A With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). Many missions of UAVs often demand V-INS in more operational environments such as indoors, hostilities, and disasters. In V-INS, inertial measurement unit (IMU) dead reckoning generates the dynamic models of UAVs, and vision sensors extract information about the surrounding environment and determine features or points of interest. With these sensors, the most widely used algorithm for estimating vehicle and feature states of V-INS is an extended Kalman filter (EKF). The design of the standard EKF does not inherently allow for time offsets between the timestamps of the IMU and vision data, and the necessary assumption of the EKF is Gaussian and white noise. In fact, sensor-related delays and measurement outliers that arise in various realistic conditions are unknown parameters. A lack of compensation of unknown parameters leads to a serious impact on the accuracy of the navigation systems. To compensate for uncertainties of the parameters, we require modified versions of the estimator or the incorporation of other techniques into the filter. The main purpose of this thesis is to develop reliable and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown parameters. First, to fuse measurements with unknown time delays, this study incorporates parameter estimation into feature initialization and state estimation. Utilizing estimated delays and cross covariance, latency-adaptive filtering corrects residual, Jacobian, and covariance. In addition, feature correspondence in image processing front end rejects vision outliers, and then a chi-squared statistic test in filtering back end detects the remaining outliers of the vision data. For frequent outliers, noise-adaptive filtering using variational approximation for Bayesian inference computes the optimal noise precision matrices of the measurement outliers. Unfortunately, few researchers have treated outlier adaptation in V-INS in great detail. Results from flight dataset tests validate the improved accuracy of V-INS employing these adaptive filtering frameworks.
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    Introducing the foundations of a general framework for closed-loop control in additive manufacturing via in situ measurements and semantic annotations
    (Georgia Institute of Technology, 2018-04-27) Garanger, Kevin
    During the last decade, additive manufacturing (AM) has become increasingly popular for rapid prototyping, but has remained relatively marginal beyond the scope of prototyping when it comes to applications with tight tolerance specifications, such as in aerospace. Despite a strong desire to supplant many aerospace structures with printed builds, additive manufacturing has largely remained limited to prototyping, tooling, fixtures, and non-critical components. There are numerous fundamental challenges inherent to additive processing to be addressed before this promise is realized. One ubiquitous challenge across all AM motifs is to develop processing-property relationships through precise, in situ monitoring coupled with formal methods and feedback control. The goal of this thesis is to justify the relevance of closed-loop control in AM, and to pave the way for the creation of a general framework to formulate AM processes as control problems where feedback can be widely adopted. Two experiments of closed-loop control in additive manufacturing for the printing of specific parts are made. These experiments are a proof of concept that feedback control is feasible in AM even without precise physics models of the processes. From this point, a idea for the generalization of closed-loop control in AM is presented via the concept of semantics for AM files and the idea of adapting the local parameters of a printed object through topology optimization.
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    Ambush games in discrete and continuous environments
    (Georgia Institute of Technology, 2017-11-22) Boidot, Emmanuel
    We consider an autonomous navigation problem, whereby a traveler aims at traversing an environment in which an adversary sets an ambush. A two players zero- sum game is introduced, describing the initial strategy of the traveler and the ambusher based on a description of the environment and the traveler initial location and desired goal. The process is single-step in the sense that agents do not reevaluate their strategy after the traveler has started moving. Players’ strategies are computed as probabilistic path distributions, a realization of which is the path chosen by the traveler and the ambush location chosen by the ambusher. A parallel is drawn between the discrete problem, where the traveler moves on a network, and the continuous problem, where the traveler moves in a compact subset of R2. Analytical optimal policies are derived. Assumptions from the Minimal Cut - Maximal Flow literature for continuous domains are used. The optimal value of the game is shown to be related to the maximum flow on the environment for sub-classes of games where the reward function for the ambusher is uniform. This proof is detailed in the discrete and continuous setups. In order to relax the assumptions for the computation of the players’ optimal strategies, a sampling-based approach is proposed, inspired by re- cent sampling-based motion planning techniques. Given a uniform reward function for the ambusher, optimal strategies of the sampled ambush game are proven to converge to the optimal strategy of the continuous ambush game under some sampling and connectivity constraints. A linear program is introduced that allows for the computation of optimal policies. The sampling-based approach is more general in the sense that it is compatible with constrained motion primitives for the traveler and non-uniform reward functions for the ambusher. The sampling-based game is used to create example applications for situ- ations where no analytic solution of the Continuous Ambush Game have been identified.This leads to more interesting games, applicable to real-world robots using modern motion planning algorithms. Examples of such games are setups where the traveler’s motion satis- fies Dubins’ kinematic constraints and setups where the reach of the ambusher is dependent on the speed of the traveler.
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    Evaluation of new enroute performance measures for air navigation service providers
    (Georgia Institute of Technology, 2017-07-27) Piquet, Helene Sophie
    In a context of steady growth of air traffic world wide, Air Navigation Service Providers must meet increasing demand and report on the quality of their performance. This research presents the design and evaluation of novel performance metrics: the relevance of ATC set of standard routes, the lateral deviation and difference in length and duration between airlines filed flight plans, actual trajectories and wind optimal routes. The proposed metrics are predicated on the necessity for the metrics to be robust, easy to compute and applicable to several different Air Traffic Management Systems, eg. Europe vs USA.
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    Resilient, multi-core and safety-critical computing architectures
    (Georgia Institute of Technology, 2017-07-27) Guillaumet, Tom
    With the onset of multi-core chips, the single-core market is closing down. Those chips constitute a new challenge for aerospace and safety-critical industries in general. Little is known about the certification of software running on these systems. There is therefore a strong need for developing embedded multi-core architectures, yet compliant with safety-criticality constraints. In this thesis, a reconfigurable multi-core architecture is described. Its suitability for executing safety-critical embedded applications is discussed. It is argued that its dynamic features allow for graceful degradation of the system, and that interference channels can be mitigated if spatial partitioning is enforced on its Network on Chip (NoC). Furthermore, the problem of the allocation of applications on the architecture is formulated as an Integer Linear Programming optimization problem. An algorithm is developed to reallocate the applications running on the fabric when hardware faults occur. The proposed algorithm enforces spatial partitioning on the Network on Chip throughout the reconfigurations. It supports multiple types of NoC topologies, constraints and hardware faults. Finally, the behavior of the presented algorithm is demonstrated in several configurations and for different scenarios of degradation of the architecture. Its performance in terms of computation time is studied, and the results indicate that its use in a real-time environment is possible.
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    A methodology to achieve microscopic/macroscopic configuration tradeoffs in cooperative multi-robot systems design
    (Georgia Institute of Technology, 2017-04-04) Durand, Jean Guillaume Dominique Sebastien
    The exponential growth experienced by the robotics sector over the past decade has fostered the proliferation of new architectures. Optimized for specific missions, these platforms are in most cases limited by their embarked computational power and a lack of full situational awareness. More robust, flexible, scalable, and inspired by nature, group robotics represent an interesting approach to overcome some limitations of these single agents and take advantage of the heterogeneity of the current robotics fleet. Their essence lies in accomplishing more complex synergistic behaviors through diversity, simple rules, and local interactions. However, the design of robotic groups is complex as decision-makers have to optimize the group operation as well as the performance of each individual unit, for the group performance. In particular, key questions arise to know whether resources should be allocated to the characteristics of the group, or to the individual capabilities of its agents in order to meet the established requirements. Current methods of swarm engineering tend to perform sequential optimization of the microscopic level (the agents) and then the macroscopic level (the group), which results in suboptimal architectures. In this context, efficiently comparing two different groups or quantifying the superiority of a group versus a single-robot design may prove impossible. Same goes of the determination of an optimal architecture for a given mission. With a special emphasis on aerial vehicles, the present research proposes to establish a methodology to achieve microscopic/macroscopic configuration tradeoffs in the design of cooperative multi-robot systems. The resulting product is the MASDeM: Multi-Agent Systems Design Methodology. A novel multi-level multi-architecture morphological approach is first introduced to facilitate design space exploration, and a mesoscopic level simulation-based design method is used to bridge the gap between microscopic and macroscopic levels. Using these first blocks, an innovative optimization technique is suggested based on two interconnected loops which differs from the classical sequential approach presently used by the research community. Results of this research show that simultaneous optimization can have clear benefits if applied to the design of multi-robot systems and on particular cases, average improvements of 16 percent were observed on the main performance metric. The proposed optimizer proves to be a key enabler for fully heterogeneous swarms, a capability which is not possible in the current paradigm. Moreover, the optimization algorithm was efficiently designed and exhibits a speedup of at least 50 percent when compared to current techniques. Finally, the exploration of the design space is effectively carried out with a combination of morphological reduction, morphological tree representation, and mesoscopic modeling. Indeed, applied to multi-robot systems, such models prove being several times faster than usual simulation approaches while remaining in the same range of accuracy.
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    Credible autocoding of control software
    (Georgia Institute of Technology, 2015-07-27) Wang, Timothy
    Formal methods is a discipline of using a collection of mathematical techniques and formalisms to model and analyze software systems. Motivated by the new formal methods-based certification recommendations for safety-critical embedded software and the significant increase in the cost of verification and validation (V\&V), this research is about creating a software development process for control systems that can provide mathematical guarantees of high-level functional properties on the code. The process, dubbed credible autocoding, leverages control theory in the automatic generation of control software documented with proofs of their stability and performance. The main output of this research is an automated, credible autocoding prototype that transforms the Simulink model of the controller into C code documented with a code-level proof of the stability of the controller. The code-level proof, expressed using a formal specification language, are embedded into the code as annotations. The annotations guarantee that the auto-generated code conforms to the input model to the extent that key properties are satisfied. They also provide sufficient information to enable an independent, automatic, formal verification of the auto-generated controller software.