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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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https://finding-aids.library.gatech.edu/agents/corporate_entities/106

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Publication Search Results

Now showing 1 - 10 of 23

Analytical Model for Sparing Policy Analysis and Optimization for Space Habitat Operations

(Georgia Institute of Technology, 2024-08) Maxwell, Andrew J. ; Ho, Koki

The inclusion of operational sparing policies in early system definition can ensure that spares’ allocations can optimally meet desired system reliabilities consistent with the planned maintenance of a crewed vehicle. This approach is critical for long-duration crewed missions where mass allocations are constrained and lack of safe abort contingencies limit options in the event of significant system degradation, especially in the environmental control and life support systems. This paper presents an analytical model for analyzing and optimizing sparing policies as part of an overall evaluation of the probability of sufficiency for a system configuration. The repair transition parameters are varied to change the state visitation probabilities that drive a change in the probability of sufficiency observed for a given mass allocation. These parameters are optimized using a particle swarm optimizer to identify the preferred strategy for a desired allocation mass.
Simultaneous Sizing of a Rocket Family with Embedded Trajectory Optimization

(Georgia Institute of Technology, 2023-12) Jo, Byeongun ; Ho, Koki

This paper presents a sizing procedure for a rocket family capable of fulfilling multiple missions, considering the commonalities between the vehicles. The procedure aims to take full advantage of sharing a common part across multiple rockets whose payload capability differs entirely, ultimately leading to cost savings in designing a rocket family. As the foundation of the proposed rocket family design method, an integrated sizing method with trajectory optimization for a single rocket is first formulated as a single optimal control problem. This formulation can find the optimal sizing along with trajectory results in a tractable manner. Building upon this formulation, the proposed rocket family design method is developed to 1) determine the feasible design space of the rocket family design problem (i.e., commonality check), and 2) if a feasible design space is determined to exist, minimize the cost function within that feasible space by solving an optimization problem in which the optimal control problem is embedded as a subproblem. A case study is carried out on a rocket family composed of expendable and reusable launchers to demonstrate the novelty of the proposed procedure.
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary

(Georgia Institute of Technology, 2023-09-26) Tomita, Kento ; Skinner, Katherine ; Ho, Koki

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.
Optimizing Multi-spacecraft Cislunar Space Domain Awareness Systems via Hidden-Genes Genetic Algorithm

(Georgia Institute of Technology, 2023-07-07) Visonneau, Lois ; Shimane, Yuri ; Ho, Koki

This paper proposes an optimization problem formulation to tackle the challenges of cislunar Space Domain Awareness (SDA) through multi-spacecraft monitoring. Due to the large volume of interest as well as the richness of the dynamical environment, traditional design approaches for Earth-based architectures are known to have challenges in meeting design requirements for the cislunar SDA; thus, there is a growing need to have a multi-spacecraft system in cislunar orbits for SDA. The design of multi-spacecraft-based cislunar SDA architecture results in a complex multi-objective optimization problem, where parameters such as number of spacecraft, observability, and orbit stability must be taken into account simultaneously. Through the use of a multi-objective hidden genes genetic algorithm, this study explores the entirety of the design space associated with the cislunar SDA problem. A demonstration case study shows that our approach can provide architectures optimized for both cost and effectiveness.
Regional Constellation Reconfiguration Problem: Integer Linear Programming Formulation and Lagrangian Heuristic Method

(Georgia Institute of Technology, 2023-07) Lee, Hang Woon ; Ho, Koki

A group of satellites, with either homogeneous or heterogeneous orbital characteristics and/or hardware specifications, can undertake a reconfiguration process due to variations in operations pertaining to Earth observation missions. This paper investigates the problem of optimizing a satellite constellation reconfiguration process against two competing mission objectives: 1) the maximization of the total coverage reward, and 2) the minimization of the total cost of the transfer. The decision variables for the reconfiguration process include the design of the new configuration and the assignment of satellites from one configuration to another. We present a novel biobjective integer linear programming formulation that combines constellation design and transfer problems. The formulation lends itself to the use of generic mixed-integer linear programming (MILP) methods such as the branch-and-bound algorithm for the computation of provably optimal solutions; however, these approaches become computationally prohibitive even for moderately sized instances. In response to this challenge, this paper proposes a Lagrangian relaxation-based heuristic method that leverages the assignment problem structure embedded in the problem. The results from the computational experiments attest to the near-optimality of the Lagrangian heuristic solutions and a significant improvement in the computational runtime as compared to a commercial MILP solver.
A Dynamic Multi-Stage Design Framework for Staged Deployment Optimization of Highly Stochastic Systems

(Georgia Institute of Technology, 2023-06-28) Hamdan, Bayan ; Liu, Zheng ; Ho, Koki ; Buyuktahtakin, Esra ; Wang, Pingfeng

The need for staged design optimization for multidisciplinary systems with strong, cross-system links and complex systems has been acknowledged in various contexts. This is prominent in fields where decisions between subsystems are dependant, as well as in cases where tactical decisions need to be made in uncertain environments. The flexibility gained by incorporating evolutionary design options has been analyzed by discretizing the time-variant uncertainties into scenarios and considering the flexible decision variables in each scenario separately. However, these problems use existing information at the decision time step. This paper presents a dynamic multi-staged design framework to solve problems that dynamically incorporate updated system information and reformulate the problem to account for the updated parameters. The importance of considering staged decisions is studied, and the benefit of the model is evaluated in cases where the stochasticity of the parameters decreases with time. The impact of considering staged deployment for highly stochastic, large-scale systems is investigated through a numerical case study as well as a case study for the IEEE 30 bus system. The case studies presented in this paper investigate multi-disciplinary design problems for large-scale complex systems as well as operational planning for highly stochastic systems. The importance of considering staged deployment for multi-disciplinary systems that have decreasing variability of their parameters with time is highlighted and demonstrated through the results of numerical and engineering case studies.
Gravity-Assist Low-Thrust Inter-System Trajectory Design with Manifold Captures

( 2022-04) Shimane, Yuri ; Ho, Koki

When traveling across multiple planetary or moon systems, invariant manifold structures may be leveraged to allow for efficient transfer within multi-body dynamics. However, the sole use of these structures is restrictive in many cases, as the manifold may not reach a desired departure or arrival location in the phase space. In particular, the use of low-thrust propulsion together with manifold dynamics requires an optimization framework that captures these mechanisms into a single problem. This work proposes an approach to design gravity-assist low-thrust transfers that leverage manifold structures at departure or arrival. The approach involves a modification to the Sims-Flanagan transcription by incorporating parametrization of arrival to a manifold Poincaré section instead of a celestial body. A key advantage is its use of two-body dynamics for the propagation of the majority of the transfer. This enables large-scale and realistic assessment of possible solutions through a combination of ODE-based propagation of the manifold, and Lagrange coefficients-based propagation of the inter-system portion of the transfer. Leveraging the proposed method, a low-thrust transfer from Earth to the Sun-Venus system, also incorporating an Earth fly-by in between, is studied.
Multidisciplinary Design Optimization Approach to Integrated Space Mission Planning and Spacecraft Design

(AIAA, 2022) Isaji, Masafumi ; Takubo, Yuji ; Ho, Koki

Space mission planning and spacecraft design are tightly coupled and need to be considered together for optimal performance; however, this integrated optimization problem results in a large-scale mixed-integer nonlinear programming (MINLP) problem, which is challenging to solve. In response to this challenge, this paper proposes a new solution approach to this problem based on decomposition-based optimization via augmented Lagrangian coordination. The proposed approach leverages the unique structure of the problem that enables its decomposition into a set of coupled subproblems of different types: a mixed-integer quadratic programming (MIQP) subproblem for mission planning, and one or more nonlinear programming (NLP) subproblem(s) for spacecraft design. Because specialized MIQP or NLP solvers can be applied to each subproblem, the proposed approach can efficiently solve the otherwise intractable integrated MINLP problem. An automatic and effective method to find an initial solution for this iterative approach is also proposed so that the optimization can be performed without a user-defined initial guess. The demonstration case study shows that, compared to the state-of-the-art method, the proposed formulation converges substantially faster and the converged solution is at least the same or better given the same computational time limit.
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

( 2021-12) Takubo, Yuji ; Chen, Hao ; Ho, Koki

This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, and space transportation scheduling. This problem involves a high-dimensional design space and is challenging especially with uncertainty present. To tackle this challenge, the developed framework has a hierarchical structure with reinforcement learning and network-based mixed-integer linear programming (MILP), where the former optimizes campaign-level decisions (e.g., design of the vehicle used throughout the campaign, destination demand assigned to each mission in the campaign), whereas the latter optimizes the detailed mission-level decisions (e.g., when to launch what from where to where). The framework is applied to a set of human lunar exploration campaign scenarios with uncertain in situ resource utilization performance as a case study. The main value of this work is its integration of the rapidly growing reinforcement learning research and the existing MILP-based space logistics methods through a hierarchical framework to handle the otherwise intractable complexity of space mission design under uncertainty. This unique framework is expected to be a critical steppingstone for the emerging research direction of artificial intelligence for space mission design.
Space Exploration Architecture and Design Framework for Commercialization

( 2021-10) Chen, Hao ; Ornik, Melkior ; Ho, Koki

The trend of space commercialization is changing the decision-making process for future space exploration architectures, and there is a growing need for a new decision-making framework that explicitly considers the interactions between the mission coordinator (i.e., government) and the commercial players. In response to this challenge, this paper develops a framework for space exploration and logistics decision making that considers the incentive mechanism to stimulate commercial participation in future space infrastructure development and deployment. By extending the state-of-the-art space logistics design formulations from the game-theoretic perspective, the relationship between the mission coordinator and commercial players is first analyzed, and then the formulation for the optimal architecture design and incentive mechanism in three different scenarios is derived. To demonstrate and evaluate the effectiveness of the proposed framework, a case study on lunar habitat infrastructure design and deployment is conducted. Results show how total mission demands and in-situ resource utilization system performances after deployment may impact the cooperation among stakeholders. As an outcome of this study, an incentive-based decision-making framework that can benefit both the mission coordinator and the commercial players from commercialization is derived, leading to a mutually beneficial space exploration between the government and the industry.