Multitree Search for Multisatellite Responsiveness Scheduling Considering Orbital Maneuvering

Zhang, Zhong; Zeyu, Nan; Jiao, Yifei; Baoyin, Hexi; Li, Junfeng

doi:10.1109/taes.2021.3129723

Cited by 13 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of latest attention to the EO community has been the concept of agile satellites that assume attitude control capability, which is deemed to enhance the overall system responsiveness and scheduling efficiency [10]. Recently, several works have explored the concept of maneuverable satellites in the domain of EO satellite systems as a new paradigm to bolster the system observation capacity by directly manipulating the orbits [1,[11][12][13]. In this paper, we investigate the concept of reconfiguration as a means for system adaptability and responsiveness that adds a new dimension to the operation of next-generation EO satellite constellation systems.…”

Section: Introductionmentioning

confidence: 99%

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

Lee¹,

Ho²

2022

Preprint

View full text Add to dashboard Cite

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 regional coverage 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 significant improvement in the computational runtime compared to a commercial MILP solver.

show abstract