Onboard mission planning for agile satellite using modified mixed-integer linear programming

She, Yuchen; Li, Shuang; Zhao, Yuchen

doi:10.1016/j.ast.2017.11.009

Cited by 64 publications

(13 citation statements)

References 20 publications

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“…To satisfy the real-time requirement of onboard autonomous scheduling, She et al [58] regard the AEOSSP as a dynamical combinatorial optimization problem, which aims to minimize the slew angle and obtain highest observation profit. A LP solver is applied to solve the established MILP model.…”

Section: Methodsmentioning

confidence: 99%

“…To further speed up the response time of the autonomous system, a long short-term memory-based encoding network and a classification network [57], an iteratively linear programming (LP) solver [58], an edge computing frameworkr [59], a hierarchical scheduling method with three stages [60], a Monte Carlo Tree search algorithm [51] are designed, respectively.…”

Section: Autonomous Modelmentioning

confidence: 99%

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Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Wang,

Wu,

Xing

et al. 2020

Preprint

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Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

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Section: Methodsmentioning

confidence: 99%

Section: Autonomous Modelmentioning

confidence: 99%

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Wang,

Wu,

Xing

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Oversubscribed, overloaded, or overconstrained scheduling problems arise naturally in many domains such as airport gate assignment [24], processor task scheduling [25], [26], and meeting scheduling [27], [28]. Perhaps most similar to the DSN scheduling problem are problems in the satellite scheduling domain, including satellite range scheduling [29], [30], telescope scheduling [31], [32], payload scheduling [33], [34], and in particular the satellite observation scheduling problem [35]- [41], which also includes the time window constraints encountered in DSN scheduling. Although the DSN scheduling problem is similar to many of the oversubscribed scheduling problems present in the literature, it has several unique properties.…”

Section: A Literature Reviewmentioning

confidence: 99%

Deep Space Network Scheduling via Mixed-Integer Linear Programming

et al. 2021

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NASA's Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its' services makes the assignment of DSN resources a daunting computational problem. In this paper we study the DSN scheduling problem, which is the problem of assigning the DSN's limited resources to its users within a given time horizon. The DSN scheduling problem is oversubscribed, meaning that only a subset of the activities can be scheduled, and network operators must decide which activities to exclude from the schedule. We first formulate this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization problem. Next, we develop a sequential algorithm which solves the resulting MILP formulation to produce valid schedules for large-scale instances of the DSN scheduling problem. We use real world DSN data from week 44 of 2016 in order to evaluate our algorithm's performance. We find that given a fixed run time, our algorithm outperforms a simple implementation of our MILP model, generating a feasible schedule in which 17% more activities are scheduled by the algorithm than by the simple implementation. We design a non-MILP based heuristic to further validate our results. We find that our algorithm also outperforms this heuristic, scheduling 8% more activities and 20% more tracking time than the best results achieved by the non-MILP implementation.

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“…The mission management problem of AEOS was first set out and analyzed by Lematre et al [6]. Beaumet et al [18], Liu et al [19] and She et al [20] studied the scheduling problem of agile satellite autonomous mission planning. Wang et al [21] connected the theory of complex networks with AEOS redundant targets scheduling problem, and proposed a fast approximate scheduling algorithm.…”

Section: Introductionmentioning

confidence: 99%

Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

Chen

et al. 2020

Preprint

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Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.

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Onboard mission planning for agile satellite using modified mixed-integer linear programming

Cited by 64 publications

References 20 publications

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

Deep Space Network Scheduling via Mixed-Integer Linear Programming

Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

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