arXiv subjects

Koki Ho

Publications and source records attributed to Koki Ho.

At least 37 records · Page 2Linked to original sources

arXiv2024

Deterministic Multistage Constellation Reconfiguration Using Integer Programming and Sequential Decision-Making Methods

In this paper, we address the problem of reconfiguring Earth observation satellite constellation systems through multiple stages. The Multi-stage Constellation Reconfiguration Problem (MCRP) aims to maximize the total observation rewards obtained by covering a set of targets of interest through the active manipulation of the orbits and relative phasing of constituent satellites. In this paper, we consider deterministic problem settings in which the targets of interest are known a priori. We propose a novel integer linear programming formulation for MCRP, capable of obtaining provably optimal solutions. To overcome computational intractability due to the combinatorial explosion in solving large-scale instances, we introduce two computationally efficient sequential decision-making methods based on the principles of a myopic policy and a rolling horizon procedure. The computational experiments demonstrate that the devised sequential decision-making approaches yield high-quality solutions with improved computational efficiency over the baseline MCRP. Finally, a case study using Hurricane Harvey data showcases the advantages of multi-stage constellation reconfiguration over single-stage and no-reconfiguration scenarios.

math.OC↗

arXiv2024

Terminal Soft Landing Guidance Law Using Analytic Gravity Turn Trajectory

This paper presents an innovative terminal landing guidance law that utilizes an analytic solution derived from the gravity turn trajectory. The characteristics of the derived solution are thoroughly investigated, and the solution is employed to generate a reference velocity vector that satisfies terminal landing conditions. A nonlinear control law is applied to effectively track the reference velocity vector within a finite time, and its robustness against disturbances is studied. Furthermore, the guidance law is expanded to incorporate ground collision avoidance by considering the shape of the gravity turn trajectory. The proposed method's fuel efficiency, robustness, and practicality are demonstrated through comprehensive numerical simulations, and its performance is compared with existing methods.

eess.SY↗

arXiv2024

Optimal Strip Attitude Command of Earth Observation Satellite using Differential Dynamic Programming

This paper addresses the optimal scan profile problem for strip imaging in an Earth observation satellite (EOS) equipped with a time-delay integration (TDI) camera. Modern TDI cameras can control image integration frequency during imaging operation, adding an additional degree of freedom (DOF) to the imaging operation. On the other hand, modern agile EOS is capable of imaging non-parallel ground targets, which require a substantial amount of angular velocity and angular acceleration during operation. We leverage this DOF to minimize various factors impacting image quality, such as angular velocity. Initially, we derive analytic expressions for angular velocity based on kinematic equations. These expressions are then used to formulate a constrained optimal control problem (OCP), which we solve using differential dynamic programming (DDP). We validate our approach through testing and comparison with reference methods across various practical scenarios. Simulation results demonstrate that our proposed method efficiently achieves near-optimal solutions without encountering non-convergence issues.

math.OC↗

arXiv2024

Time Efficient Rate Feedback Tracking Controller with Slew Rate and Control Constraint

This paper proposes a time-efficient attitude-tracking controller considering the slew rate constraint and control constraint. The algorithm defines the sliding surface, which is the linear combination of command, body, and regulating angular velocity, and utilizes the sliding surface to derive the control command that guarantees finite time stability. The regulating rate, which is an angular velocity regulating the attitude error between the command and body frame, is defined along the instantaneous eigen-axis between the two frames to minimize the rotation angle. In addition, the regulating rate is shaped such that the slew rate constraint is satisfied while the time to regulation is minimized with consideration of the control constraint. Practical scenarios involving Earth observation satellites are used to validate the algorithm's performance.

math.OC↗

arXiv2024

Analysis and Design of Satellite Constellation Spare Strategy Using Markov Chain

This paper introduces the analysis and design method of an optimal spare management policy using Markov chain for a large-scale satellite constellation. We propose an analysis methodology of spare strategy using a multi-echelon $(r,q)$ inventory control model with Markov chain, and review two different spare strategies: direct resupply, which inserts spares directly into the constellation orbit using launch vehicles; and indirect resupply, which places spares into parking orbits before transferring them to the constellation orbit. Furthermore, we propose an optimization formulation utilizing the results of the proposed analysis method, and an optimal solution is found using a genetic algorithm.

math.OC↗

arXiv2024

Hierarchical Framework for Space Exploration Campaign Schedule Optimization

Space exploration plans are becoming increasingly complex as public agencies and private companies target deep-space locations, such as cislunar space and beyond, which require long-duration missions and many supporting systems and payloads. Optimizing multi-mission exploration campaigns is challenging due to the large number of required launches as well as their sequencing and compatibility requirements, making the conventional space logistics formulations not scalable. To tackle this challenge, this paper proposes an alternative approach that leverages a two-level hierarchical optimization algorithm: a genetic algorithm is used to explore the campaign scheduling solution space, and each of the solutions is then evaluated using a time-expanded multi-commodity flow mixed-integer linear program. A number of case studies, focusing on the Artemis lunar exploration program, demonstrate how the method can be used to analyze potential campaign architectures. The method enables a potential mission planner to study the sensitivity of a campaign to program-level parameters such as logistics vehicle availability and performance, payload launch windows, and in-situ resource utilization infrastructure efficiency.

math.OC↗

arXiv2024

Orbital Facility Location Problem for Satellite Constellation Servicing Depots

This work proposes an adaptation of the Facility Location Problem for the optimal placement of on-orbit servicing depots for satellite constellations in high-altitude orbit. The high-altitude regime, such as Medium Earth Orbit (MEO), is a unique dynamical environment where existing low-thrust propulsion systems can provide the necessary thrust to conduct plane-change maneuvers between the various orbital planes of the constellation. As such, on-orbit servicing architectures involving servicer spacecraft that conduct round-trips between servicing depots and the client satellites of the constellation may be conceived. To this end, orbital facility location problem is a binary linear program, where the costs of operating and allocating the facility(ies) to satellites are considered in terms of the sum of Equivalent Mass to Low Earth Orbit (EMLEO), is proposed. The low-thrust transfers between the facilities and the clients are computed using a parallel implementation of a Lyapunov feedback controller. The total launch cost of the depot along with its servicers, propellant, and payload are taken into account as the cost to establish a given depot. The proposed approach is applied to designing on-orbit servicing depots for the Galileo and the GPS constellations.

eess.SY↗

arXiv2024

Sensitivity Analysis of Separation Time Along Weak Stability Boundary Transfers

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.

nlin.CD↗

arXiv2023

Multi-Spacecraft Predictive Sensor Tasking for Cislunar Space Situational Awareness

This paper delves into the predictive sensor tasking algorithm for the multi-observer, multi-target sensor setting, leveraging the Extended Information Filter (EIF). Conventional predictive formulations suffer from the curse of dimensionality due to the dependence of the performance metric on the target-observer assignment history. This paper exploits the EIF's additive structure of measurement information to break the dependence and devises an efficient linear integer programming formulation. We further investigate the resulting formulation to study how the cislunar dynamics expands and shrinks the measurement information, and discuss when the information gain is maximized in relation to the observation space and the uncertainty deformation caused by the dynamics. We numerically demonstrate that the predictive sensor tasking algorithm outperforms the myopic algorithm in two different metrics, depending on the formulation.

eess.SY↗

arXiv2023

Regional Constellation Reconfiguration Problem: Integer Linear Programming Formulation and Lagrangian Heuristic Method

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: (i) the maximization of the total coverage reward and (ii) 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 bi-objective 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 compared to a commercial MILP solver.

math.OC↗

arXiv2023

Stochastic Hazard Detection For Landing Under Topographic Uncertainty

Autonomous hazard detection and avoidance is a key technology for future landing missions in unknown surface conditions. Current state-of-the-art stochastic algorithms assume simple Gaussian measurement noise on dense, high-fidelity digital elevation maps, limiting the algorithm's applicability. This paper introduces a new stochastic hazard detection algorithm capable of more general topographic uncertainty by leveraging the Gaussian random field regression. The proposed approach enables the safety assessment with imperfect and sparse sensor measurements, which allows hazard detection operations under more diverse conditions. We demonstrate the performance of the proposed approach on the existing Mars digital terrain models.

cs.RO↗

arXiv2023

Analysis of Cooperative and Non-Cooperative Architectures for Multi-Plane On-Orbit Refueling

As many satellite constellations are proposed, deployed, and operated, their maintenance becomes increasingly important to provide satisfactory services; therefore, on-orbit refueling to spacecraft has become one of the most promising technologies for realizing more sustainable space development. This paper develops an analytical model to examine two types of mission architectures for multi-target on-orbit refueling missions: a non-cooperative architecture and a cooperative architecture. In the (rather conventional) non-cooperative refueling architecture, a servicer spacecraft visits passive targets one by one, whereas, in the cooperative refueling architecture, both the servicer and the targets can actively maneuver to complete refueling cooperatively. This paper analytically compares the fuel mass required in each architecture to support the decision-making process of mission architects. Furthermore, the condition under which the cooperative architecture becomes more efficient than the non-cooperative architecture is analytically derived. The sensitivities of this condition against key mission parameters, such as the number of targets and their inclination, are also analyzed through a case study of multi-plane multi-target on-orbit refueling in low Earth orbits.

math.OC↗

arXiv2023

Optimizing Multi-Spacecraft Cislunar Space Domain Awareness Systems via Hidden-Genes Genetic Algorithm

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.

physics.space-ph↗

arXiv2023

Deep Monocular Hazard Detection for Safe Small Body Landing

Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. Current state-of-the-practice methods rely on high-fidelity, a priori terrain maps, which require extensive human-in-the-loop verification and expensive reconnaissance campaigns to resolve mapping uncertainties. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image, thus reducing reliance on high-fidelity, a priori data products. We demonstrate precise and accurate safety mapping performance on real in-situ imagery of prospective sample sites from the OSIRIS-REx mission.

cs.CV↗

arXiv2022

Multidisciplinary Design Optimization Approach to Integrated Space Mission Planning and Spacecraft Design

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 MINLP problem by iterative solving a set of coupled subproblems via the augmented Lagrangian coordination approach following the philosophy of Multi-disciplinary Design Optimization (MDO). 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. Since 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 the need for a user-defined initial guess. In the demonstration case study, a human lunar exploration mission sequence is optimized with a subsystem-level parametric spacecraft design model. Compared to the state-of-the-art method, the proposed formulation can obtain a better solution with a shorter computational time even without parallelization. For larger problems, the proposed solution approach can also be easily parallelizable and thus is expected to be further advantageous and scalable.

math.OC↗

arXiv2022

Space Exploration Architecture and Design Framework for Commercialization

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.

math.OC↗

arXiv2022

Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

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.

cs.LG↗

arXiv2021

Flexibility Management for Space Logistics via Decision Rules

This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multi-stage 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.

math.OC↗