Multi-objective optimization for multi-satellite scheduling task

Khojah, Heba Abdulrahman; Mosa, Mohamed Atef

doi:10.52465/joscex.v3i1.71

Cited by 4 publications

(1 citation statement)

References 15 publications

(19 reference statements)

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“…Most satellites receive dynamic instructions on how to execute their mission in orbit, and planning the exact sequence of tasks is critical to the efficiency and sustainability of the project [4]. Algorithmic optimization solutions have been suggested [5]- [7] as a remedy to the planning problem, and with the rise of quantum technologies, there is a need for exploring how quantum computing can improve the time complexity or the quality of these solutions.…”

Section: Introductionmentioning

confidence: 99%

Quantum algorithms applied to satellite mission planning for Earth observation

Rainjonneau¹,

Tokarev²,

Iudin³

et al. 2023

Preprint

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Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there are also limitations; satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit. Therefore, it is critical that satellites are employed efficiently. This poses a challenge known as the satellite mission planning problem, which could be computationally prohibitive to solve on large scales. However, close-to-optimal algorithms can often provide satisfactory resolutions, such as greedy reinforcement learning, and optimization algorithms. This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far. The problem is formulated as maximizing the number of high-priority tasks completed on real datasets containing thousands of tasks and multiple satellites. This work demonstrates that through solution-chaining and clustering, optimization and machine learning algorithms offer the greatest potential for optimal solutions. Most notably, this paper illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, which is a significant improvement over the baseline greedy methods with a completion rate of 63.6%. The results presented in this work pave the way to quantumenabled solutions in the space industry and, more generally, future mission planning problems across industries.

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Section: Introductionmentioning

confidence: 99%