# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 1 Introduction
In a distributed architecture, each node in the system has autonomous capabilities. Child nodes not only act as executors of instructions, but also undertake certain computational tasks. The distributed architecture can be further divided into the centralized-distributed architecture and the fully distributed architecture. In a centralized-distributed architecture, there exists one or more master agents that divide the multiple agents into different groups for control, while each agent can make decisions independently. [PERSON] et al. [13] proposed a distributed broken-chain reconnection algorithm, where the master satellite splits the scheduling task, transmits sub-tasks to the neighboring auxiliary satellites for computation, and then the child satellites return the scheduled results to the master satellite. The approach can break through the limitations of single satellite computing resources and improve in-orbit computing capability. However, the algorithm can only perform task planning for a single satellite. Contract network protocol (CNP) [14] is a classic centralized-distributed algorithm. CNP adopts the market auction mechanism to allocate tasks in sequence. It has been widely studied in the field of satellite mission planning. [PERSON] et al. [15] divided the satellite mission planning problem into two levels: satellite cluster mission planning and single-satellite autonomous planning, and proposed an improved CNP algorithm. [PERSON] et al. [16] proposed a multi-satellite collaborative mission planning architecture based on the improved Shuffled Frog Leaping Algorithm (SFLA). The master satellite is responsible for task allocation, while child satellites are responsible for task scheduling and calculation of fitness values. Although the child nodes in the centralized-distributed architecture take on a certain amount of computational work, it still puts high demands on the master satellite's computational capability, and there is still a problem of poor system robustness. The fully distributed architecture, on the other hand, does not have a central node, and the nodes generate solutions by mutual negotiation. Determining how to achieve consistency in solutions through decentralized communication is a key issue for the architecture. [PERSON] et al. [17] addressed collaborative planning scenarios for child satellites after the failure of the master satellite. The Utility-based Regret play was proposed as a negotiation mechanism for teams in a centralized-distributed structure, and the Smoke Signal play and the Broadcast-based play were used as negotiation mechanisms for teams in a fully distributed structure. [PERSON] et al. [18] proposed a multi-agent collaborative coevolutionary genetic algorithm, where multiple subpopulations distributed on different satellites evolve in parallel to continuously optimize the combination of satellite schemes. [PERSON] et al. [19] proposed a distributed algorithm based on the Hybrid Dynamic Mutation Genetic Algorithm (HDMGA) with a local search heuristic module and a distributed global optimization module. However, as the constellation and mission scale increase, the computational efficiency of the metaheuristic algorithm for iterative optimization is greatly affected. Therefore, considering the limitations of on-board computing power and inter-satellite communication conditions, we need to explore a low complexity and low communication cost algorithm to ensure efficient task allocation. [PERSON] et al. [20] proposed the Consensus-based Bundle Algorithm (CBBA), which employed a market auction mechanism for decentralized task selection and a consensus strategy based on local communication to resolve conflicts. The algorithm can accomplish task planning within polynomial time while ensuring the quality of planning. [PERSON] et al. [21] introduced CBBA into the imaging satellite mission planning problem, solved the coupling constraint problem in mission planning, and explored the fitness functions in different scenarios.