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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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Publication Search Results

Now showing 1 - 10 of 11

Maximizing Observation Throughput via Multi-Stage Satellite Constellation Reconfiguration

(Georgia Institute of Technology, 2022-08) Lee, Hang Woon ; Chen, Hao ; Ho, Koki

We examine the problem of multi-stage satellite constellation reconfiguration in the domain of Earth observations. The goal of the problem is to maximize the total system observation throughput by actively manipulating the orbits and the relative phasing of the constituent satellites. We propose a novel integer linear programming formulation of the problem that is constructed based on the concept of time-expanded networks. To tackle the computational intractability arising due to the combinatorial explosion of the solution space, we propose two decomposition based algorithmic frameworks based on the principles of the myopic policy and the rolling horizon procedure. We empirically present that these heuristics produce high-quality solutions relative to optimal solutions. We conduct computational experiments to demonstrate the value of the proposed work.
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.
On-Orbit Servicing Logistics Framework Generalized to the Multi-Orbit Case

(Georgia Institute of Technology, 2021-08) Sarton du Jonchay, Tristan ; Shimane, Yuri ; Isaji, Masafumi ; Chen, Hao ; Ho, Koki

This paper proposes a multi-orbit on-orbit servicing logistics optimization frame-work capable of planning the operations of sustainable servicing infrastructures with client satellites distributed across different orbits of various shapes. The pro-posed framework generalizes the state-of-the-art network-based on-orbit servic-ing logistics optimization method to the multi-orbit case by tracking the relative motion of the network nodes as part of the process of computing the costs of the network arcs. The new framework keeps track of the simulation time in order to propagate the orbital elements of the network nodes over time. The orbital ele-ments are then inputted into high-thrust and low-thrust trajectory optimization routines interfaced with the framework to accurately compute the cost of trans-portation of the servicers. Finally, a mixed-integer linear program is formulated to model the operations of the servicing infrastructure over the network and over time, whereas the rolling horizon procedure is leveraged to account for the uncer-tainties in service demand. Two case studies demonstrate the application of the generalized framework to the short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.
On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

( 2021-07) Sarton Du Jonchay, Tristan ; Chen, Hao ; Isaji, Masafumi ; Shimane, Yuri ; Ho, Koki

This paper proposes an on-orbit servicing logistics optimization framework capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-the-art on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. Mixed-integer linear programming is leveraged to find the optimal operations of the servicers over a given period, whereas the rolling horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high- and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.
Framework for Modeling and Optimization of On-Orbit Servicing Operations under Demand Uncertainties

( 2021-06) Sarton Du Jonchay, Tristan ; Chen, Hao ; Gunasekara, Onalli ; Ho, Koki

This paper develops a framework that models and optimizes the operations of complex on-orbit servicing infrastructures involving one or more servicers and orbital depots to provide multiple types of services to a fleet of geostationary satellites. The proposed method extends the state-of-the-art space logistics technique by addressing the unique challenges in on-orbit servicing applications and integrates it with the Rolling Horizon decision-making approach. The space logistics technique enables modeling of the on-orbit servicing logistical operations as a Mixed-Integer Linear Program whose optimal solutions can efficiently be found. The Rolling Horizon approach enables the assessment of the long-term value of an on-orbit servicing infrastructure by accounting for the uncertain service needs that arise over time among the geostationary satellites. Two case studies successfully demonstrate the effectiveness of the framework for 1) short-term operational scheduling and 2) long-term strategic decision making for on-orbit servicing architectures under diverse market conditions.
Interdisciplinary Space Logistics Optimization Framework for Large-Scale Space Exploration

(Georgia Institute of Technology, 2021-04-28) Chen, Hao

As low-cost rocket launch technologies and space resource utilization systems emerge, human space exploration is attracting increasing interest from industry, government, and academia. To extend the domain of human activity beyond the low-Earth orbit and maintain a long-term human presence in cislunar space and eventually Mars, we need to build a sustainable and affordable interplanetary space transportation system. It requires a campaign-level perspective for space mission design in addition to the conventional mission-level perspective. This thesis first proposes an integrated space logistics framework to enable concurrent optimization of space transportation scheduling, spacecraft sizing, space infrastructure design and deployment. Then, a periodic time-expanded network is built to resolve the scalability issue in the time dimension for long-term space exploration missions. After establishing efficient space logistics optimization frameworks, we switch our focus to space infrastructure technology trade studies to consider space infrastructure design from the subsystem-level. A multi-fidelity optimization method is introduced to guarantee optimization accuracy while improving computational efficiency. Finally, a flexibility management framework is proposed to handle uncertainties in space mission planning and operations. Multiple case studies for human lunar and Mars exploration campaigns are conducted leveraging the proposed methods and frameworks to demonstrate their values and potential impacts. This research resolves the grand challenge of space logistics mission design for future large-scale multi-mission space campaigns.
Flexibility Management for Space Logistics via Decision Rules

( 2021-04) Chen, Hao ; Gardner, Brian M. ; Grogan, Paul T. ; Ho, Koki

This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multistage stochastic programming. It aims to add built-in flexibility to space architectures in the phase of early-stage mission planning. The proposed framework integrates the decision rule formulation into a network-based space logistics optimization formulation model. It can output a series of decision rules and generate a Pareto front between the expected mission cost (i.e., initial mass in low Earth orbit) and the expected mission performance (i.e., effective crew operating time), considering the uncertainty in the environment and mission demands. The generated decision rules and the Pareto front plot can help decision makers create implementable policies immediately when uncertainty events occur during space missions. An example mission case study about space station resupply under rocket launch delay uncertainty is established to demonstrate the value of the proposed framework.
Multifidelity Space Mission Planning and Infrastructure Design Framework for Space Resource Logistics

( 2021-03) Chen, Hao ; Sarton Du Jonchay, Tristan ; Hou, Linyi ; Ho, Koki

To build a sustainable space transportation system for human space exploration, the design and deployment of space infrastructure, such as in-situ resource utilization plants, in-orbit propellant depots, and on-orbit servicing platforms, are critical. The design analysis and trade studies for these space infrastructure systems require the consideration of not only the design of the infrastructure elements themselves, but also their supporting systems (e.g., storage, power) and logistics transportation while exploring various architecture options (e.g., location, technology). This paper proposes a system-level space infrastructure and logistics design optimization framework to perform architecture trade studies. A new space-infrastructure logistics optimization problem formulation is proposed that considers the internal interactions of infrastructure subsystems and their external synergistic effects with space logistics simultaneously. Because the full-size version of this proposed problem formulation can be computationally prohibitive, a new multifidelity optimization formulation is developed by varying the granularity of the commodity-type definition over the space logistics network; this multifidelity formulation can find an approximate solution to the full-size problem computationally efficiently with little sacrifice in the solution quality. The proposed problem formulation and method are applied to the design of in situ resource utilization systems in a multimission lunar exploration campaign to demonstrate their values.
Space architecture design for commercial suitability: A case study in in-situ resource utilization systems

( 2020-10) Sarton Du Jonchay, Tristan ; Chen, Hao ; Wieger, Anna ; Szajnfarber, Zoe ; Ho, Koki

Space Agencies are increasingly interested in stimulating non-traditional players to participate more broadly in the space enterprise. Historically, high barriers to entry in the space market have included challenges of working with the government customer and high technical and financial risks associated with the complexity of space exploration. More recently, agencies have used inducements (e.g., new contracting mechanisms, access to testing facilities) to mitigate these barriers. While these efforts mainly focused on reducing barriers to participation in existing exploration architectures, this paper explores the viability of an alternative strategy. Instead of providing inducements, which essentially subsidize participation, we propose a new strategy for space agencies to treat “commercial suitability” as another “-ility” and make it an explicit criterion of the initial architecture selection. This can be an effective option when multiple equivalent architectures (as evaluated against traditional cost, schedule, and performance measures) differ on their “commercial suitability.” As a proof-of-concept for this strategy, we develop a case study with lunar in-situ resource utilization plant systems as a basis for comparing the architectures with dedicated mass-wise optimal design (selected using traditional architecting strategies) vs. standardized mass-produced modular ISRU (selected using commercially-suitable strategies). The results show that architecture selection that considers commercial suitability upfront can achieve increased commercial participation without compromising cost performance compared with the baseline architecture. This serves as an existence proof for the potential value of this new strategy.