# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 1 Introduction  
Satellite remote sensing aims to obtain information from the Earth's surface. It has been widely used in geography, Earth science, meteorology, military, etc. [1]. In traditional imaging missions, a single low Earth orbit satellite is often used to take images of multiple targets multiple times. However, the increasing number of missions and the higher time resolution requirements call for more satellite members [2]. Besides, different missions require different kinds of payload, but multiple payloads can hardly be carried by one satellite. Furthermore, multiple payloads can hardly work together at the same time in the narrow imaging time window. Therefore, current remote sensing missions rely more and more on large satellite clusters, which brings new challenges to mission assignment and planning [3]. A multi-agent system (MAS) is the main solution to the distributed autonomy problem for multiple satellites. Agent technology was first used in DeepSpace-1 (DS-1) [4, 5]. Although MAS applied in DS-1 is a single satellite system, with its success the MAS started to be wildly used in satellite cluster task planning. [PERSON] [6, 7, 8] summarized the work of predecessors, and defined four different working modes of satellite cluster, including traditional mode, top-down mode, centralized mode, and distribute mode. The traditional mode and the top-down mode were used in the early satellite clusters. For instance, [PERSON] [9] proposed an edge computing framework including a central node and several edge nodes using a constructive heuristic algorithm based on the density of residual tasks. Although these algorithms have achieved good results, the central node (ground station or central master satellite) works very hard. Current research mainly focuses on the latter two modes: the centralized mode and the distributed mode. [PERSON] [10] developed a satellite cluster negotiation and mission assignment method by using contract net in the centralized mode. He proposed three negotiation models: acquaintance's trust-basedannouncing bidding, adaptive bidding with swarm intelligence, and a multi-attribute decision-based fuzzy evaluation bidding method. [PERSON] [11] discussed the cooperative multicenter problem on satellite imaging scheduling and proposed a cooperative co-evolutionary algorithm, and then proposed a novel fixed-length binary encoding mechanism for missions assignment. [PERSON] [12] designed a self-organizing multi-agent structure belong to the distribution mode. The system can adapt to the change of the problems and can synchronize the satellites' plans in order to avoid duplication. In order to improve cluster consistency, [PERSON] [13] designed a distributed time consensus protocol for multi-agent systems with general linear dynamics on a directed graph. [PERSON] [14] proposed an agent-based framework to study the self-organization of the satellite cluster, which can establish a temporary alliance to change the modularity of the system. Since a remote sensing mission is usually divided into multiple stages (discovery, identification, confirmation, tracking, and monitoring) and one stage can further be divided into multiple steps (collection, transmission, process, etc. ), a micro-satellite is not able to finish a mission all by itself. Current research has begun to focus on the cooperation of satellite clusters toward finishing one complex mission instead of merely using one satellite. [PERSON] [15] used multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization, and iteration to realize the coordination of multiple satellites and multiple observations of the same target. [PERSON] [16] designed autonomous operational schemes for Earth-observing swarms of nano-satellites, in which all satellites cooperate to observe multiple targets. [PERSON] [17] presented an agent-based simulation framework. In his study, satellites could work together to perform a set of observational tasks.