OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18173 Abstract-Mission planning for a constellation of Earth observation satellites is a complex problem raising significant technological challenges for tomorrow's space systems. The large numbers of customers requests and their dynamic introduction in the planning system result in a huge combinatorial search space with a potentially highly dynamical evolution requirements. The techniques used nowadays have several limitations, in particular, it is impossible to dynamically adapt the plan during its construction even for small modifications. Satellites of a constellation are planned in a chronological way instead of a more collective planning which can provide additional load balancing.In this paper, we propose to solve this difficult and highly dynamic problem using adaptive multi-agent systems, taking advantage from their self-adaptation and self-organization mechanisms. In the proposed system, the agents, through their local interactions, allow to dynamically reach a good solution, while ensuring a controlled distribution of tasks within the constellation of satellites. Finally, a comparison with a classical chronological greedy algorithm, commonly used in the spatial domain, highlights the advantages of the presented system.

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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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