Optimal Multi-stage Reconfiguration of Earth Observation Satellite Constellations Using Integer Linear Programming and Sequential Decision-Making

Khái niệm cốt lõi

The key objective is to devise optimal sequences of orbital maneuvers for satellites in a constellation system to maximize the total observation rewards obtained by covering a set of targets of interest over multiple reconfiguration stages.

Tóm tắt

The paper addresses 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.
The authors propose a novel integer linear programming (ILP) formulation for MCRP, capable of obtaining provably optimal solutions. To overcome computational intractability due to the combinatorial explosion in solving large-scale instances, they introduce two computationally efficient sequential decision-making methods based on the principles of a myopic policy and a rolling horizon procedure.
The key highlights and insights are:

MCRP extends prior work on single-stage reconfiguration by allowing multiple reconfiguration opportunities, providing increased flexibility and responsiveness.
The ILP formulation models the sequence of orbital maneuvers using a time-expanded graph and considers various physical and operational constraints.
The myopic policy and rolling horizon policy are proposed as computationally efficient sequential decision-making methods to address large-scale MCRP instances.
Computational experiments demonstrate that the devised sequential decision-making approaches yield high-quality solutions with improved computational efficiency over the baseline MCRP.
A case study using Hurricane Harvey data showcases the advantages of multi-stage constellation reconfiguration over single-stage and no-reconfiguration scenarios.

Thống kê

The mission planning horizon comprises 𝑇 discrete time steps.
The constellation has 𝐾 satellites and 𝑃 target points of interest.
The reconfiguration process consists of 𝑁 stages.
Each satellite 𝑘 has a maximum resource (e.g., propellant) budget of 𝑐𝑘
max.
The minimum number of satellites required to cover target point 𝑝 at time 𝑡 is 𝑟𝑡𝑝.
The observation reward for target point 𝑝 at time 𝑡 is 𝜋𝑡𝑝.

Trích dẫn

"The objective of MCRP is to determine the optimal sequence of orbital maneuvers for satellites, aimed at maximizing the observation rewards obtained by covering targets of interest."
"The MCRP framework is versatile enough to be applied to scheduling either a short segment of the entire mission horizon or the entire mission horizon itself, depending on the users' requirements."
"Multiple, short-segmented MCRPs can be consecutively applied, and their solutions can be concatenated to generate a solution for a longer planning horizon. However, this approach does not ensure optimality across the entire mission."

Thông tin chi tiết chính được chắt lọc từ

Deterministic Multi-stage Constellation Reconfiguration Using Integer Linear Programming and Sequential Decision-Making Methods

by Hang Woon Le... lúc arxiv.org 05-01-2024

https://arxiv.org/pdf/2401.11567.pdf

Yêu cầu sâu hơn

How can the proposed MCRP framework be extended to handle uncertainties in target locations, observation rewards, and satellite capabilities

To extend the proposed MCRP framework to handle uncertainties in target locations, observation rewards, and satellite capabilities, a stochastic optimization approach can be employed. This involves incorporating probabilistic models for uncertain parameters, such as target locations and observation rewards, into the MCRP formulation. This can be achieved by introducing scenario-based optimization, where multiple scenarios representing different possible realizations of uncertainties are considered. The objective would then be to optimize the decision variables under all scenarios to ensure robustness against uncertainty.
For uncertain target locations, a scenario generation method can be used to create a set of potential target location scenarios based on historical data, predictive models, or expert knowledge. Observation rewards can be modeled as random variables with known probability distributions, allowing for the consideration of uncertainty in reward outcomes. Satellite capabilities, such as propulsion constraints or sensor limitations, can also be modeled stochastically to account for variability in satellite performance.
By incorporating uncertainties into the MCRP framework, decision-makers can make more informed decisions that are robust to variations in target locations, observation rewards, and satellite capabilities. This enhanced framework would provide a more realistic representation of the dynamic and uncertain nature of Earth observation satellite constellation reconfiguration.

What are the potential trade-offs between the myopic policy and the rolling horizon policy in terms of solution quality and computational efficiency

The myopic policy and the rolling horizon policy offer different approaches to solving the MCRP, each with its own set of trade-offs in terms of solution quality and computational efficiency.
The myopic policy, by solving each stage as a separate subproblem without considering future stages, simplifies the optimization process and reduces the computational complexity of each subproblem. This can lead to faster computation times and easier implementation, especially for large-scale instances. However, the myopic policy may suffer from suboptimality, as decisions made in earlier stages are not reconsidered in light of future information.
On the other hand, the rolling horizon policy considers a lookahead of multiple stages, allowing for more informed decisions in the current stage based on future expectations. This can potentially lead to higher solution quality by taking into account the impact of current decisions on future stages. However, the rolling horizon policy may require more computational resources and time to solve due to the larger subproblem size and the need to iterate through multiple stages.
In summary, the myopic policy prioritizes computational efficiency and simplicity, while the rolling horizon policy focuses on solution quality and long-term optimization. The choice between the two policies would depend on the specific requirements of the MCRP instance, balancing the need for optimal solutions with computational resources available.

How can the MCRP formulation be adapted to incorporate additional mission-specific constraints, such as coverage continuity requirements or inter-satellite communication constraints

To adapt the MCRP formulation to incorporate additional mission-specific constraints, such as coverage continuity requirements or inter-satellite communication constraints, the following modifications can be made:

Coverage Continuity Requirements: Constraints can be added to ensure continuous coverage of targets over consecutive stages. This can be achieved by introducing constraints that enforce a minimum overlap of coverage between consecutive stages or by penalizing discontinuities in coverage. The objective function can be adjusted to prioritize solutions that maintain coverage continuity throughout the mission planning horizon.

Inter-Satellite Communication Constraints: If inter-satellite communication is a critical factor in constellation reconfiguration, constraints can be included to model communication links between satellites. These constraints can restrict the assignment of satellites to orbital slots based on communication range or bandwidth limitations. The optimization objective can be modified to maximize communication efficiency or minimize communication delays between satellites.

By incorporating these mission-specific constraints into the MCRP formulation, the optimization process can be tailored to address the unique requirements of the Earth observation satellite constellation reconfiguration problem. This customization ensures that the solutions obtained are not only optimal in terms of observation rewards but also compliant with operational constraints and objectives.