Organizational Unit:

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

Organizational Unit

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 42

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.
Model Predictive Path Integral Control for Spacecraft Rendezvous Proximity Operations on Elliptic Orbits

(Georgia Institute of Technology, 2023-08) Sasaki, Tomohiro ; Ho, Koki ; Lightsey, E. Glenn

This paper presents a nonlinear control framework for spacecraft rendezvous and proximity operations on elliptic orbits using Model Predictive Path Integral (MPPI) control. Path integral control is a sampling-based nonlinear stochastic optimal control algorithm that can avoid linear and quadratic approximations in both dynamics and cost functions. While this control method has gained popularity in the robotics community due to its algorithmic effectiveness, it remains unexplored in astrodynamics. This paper demonstrates a comprehensive closed-loop simulation of spacecraft rendezvous employing MPPI and evaluates its control performance through these simulations.
Translunar Logistics with Low-Energy Transfers

(Georgia Institute of Technology, 2023-08) Gollins, Nick ; Shimane, Yuri ; Ho, Koki

Low-energy lunar transfers (LETs) utilize three-body mechanics with fourth-body (solar) perturbations to provide an alternative to direct lunar transfers. The offer of reduced lunar orbit insertion cost in exchange for longer time-of-flight and potentially higher transfer insertion cost presents an interesting trade-off when planning the logistics of multi-mission lunar exploration campaigns. This is particularly true for logistics featuring spacecraft with a variety of launch vehicles and propellant types, as the logistics of each spacecraft are impacted by the costs and benefits of LETs differently. This paper presents a translunar logistics model featuring LETs, discusses the trade-offs versus direct transfers through some case studies, and highlights the scenarios in which LETs prove most useful.
Optimization of Earth-Moon Low-Thrust-Enhanced Low-Energy Transfer

(Georgia Institute of Technology, 2023-08) Takubo, Yuji ; Shimane, Yuri ; Ho, Koki

This work proposes an optimization method for the novel class of lunar transfer that leverages both low-thrust acceleration and weak stability boundary effects simultaneously. Such translunar orbits are aimed at filling the gap that exists in conventional transfer options in the trade-off between the time of flight and mass ratio. We first generate the candidates for the initial guess via backward propagation from a cislunar periodic orbit. These trajectories are corrected into feasible solutions, then further optimized based on a multiple-shooting method with a Sims-Flanagan transcription. The obtained transfer time of the solutions is around 45-70 days, which is almost half of the traditional ballistic transfers (90-110 days) with a few percent increase in its propellant mass, showing a huge benefit of performing the low-thrust propulsion in the Earth-Moon low-energy transfer.
Characterizing Low-Thrust Transfers from Near-Rectilinear Halo Orbits to Low Lunar Orbits with Q-Law

(Georgia Institute of Technology, 2023-08) Shimane, Yuri ; Preston, Dyllon ; Ho, Koki

Near rectilinear halo orbits (NRHOs) are an integral orbital regime in humanity's permanent return to cislunar space. Traffic between the NRHO and low-lunar orbit (LLO) is expected to increase dramatically, supporting cislunar activities. Linking NRHOs and LLOs via low-thrust transfers will be a vital piece of transportation infrastructure. This work provides an assessment of low-thrust transfers from NRHOs to LLOs using Q-law, a Lyapyunov feedback controller based on Keplerian elements, treating the Earth as a third-body perturbation. Leveraging the deterministic nature of Q-law, low-thrust transfers between NRHOs and LLOs are characterized for various propulsion systems, spacecraft mass, and departure windows.
Sensitivity Analysis of Separation Time Along Weak Stability Boundary Transfers

(Georgia Institute of Technology, 2023-08) Nolton, Isabel ; Tomita, Kento ; Shimane, Yuri ; Lee, Hang Woon ; Ho, Koki

This study analyzes the sensitivity of the dynamics around Weak Stability Boundary Transfers (WSBT) in the elliptical restricted three-body problem. With WSBTs increasing popularity for cislunar transfers, understanding its inherently chaotic dynamics becomes pivotal for guiding and navigating cooperative spacecrafts as well as detecting non-cooperative objects. We introduce the notion of separation time to gauge the deviation of a point near a nominal WSBT from the trajectory's vicinity. Employing the Cauchy-Green tensor to identify stretching directions in position and velocity, the separation time, along with the Finite-Time Lyapunov Exponent are studied within a ball of state uncertainty scaled to typical orbit determination performances.
Learning Reachability for Hazard Detection Avoidance in Planetary Landing

(Georgia Institute of Technology, 2023-08) Tomita, Kento ; Jo, Beyong-Un ; Ho, Koki

Autonomous hazard detection and avoidance (HD&A) poses a stochastic perceptionaware guidance problem, where the visible surface depends on the trajectory, and the safest target locations are kept updated. For the concurrent optimization of the target and trajectory, evaluating the reachable surface under guidance constraints in real-time is critical, but it requires solving optimization problems multiple times. To bypass the optimization-based computation of the reachable surface, we propose to learn the parameterized reachable surface by a neural network, which ultimately enables the reachability-aware guidance algorithms. This paper presents the proposed parameterization method and validation results by numerical simulations.
Cislunar Satellite Constellation Design via Integer Linear Programming

(Georgia Institute of Technology, 2023-08) Patel, Malav ; Shimane, Yuri ; Ho, Koki

Cislunar space awareness is of increasing interest to the international community as Earth-Moon traffic is projected to increase. This raises the problem of placing satellites optimally in a constellation to provide satisfactory coverage for said traffic. The Circular Restricted 3 Body Problem (CR3BP) provides promising periodic orbits in the Earth-Moon rotating frame for traffic monitoring. This work converts a spatially and temporally varying traffic coverage requirement into an integer linear programming problem, attempting to minimize the number of satellites required for the requested coverage.