Self-Organizing Method on Mission-Level Task Allocation of Large-Scale Remote Sensing Satellite Swarm

Abstract

Distributed self-organization and self-management is an ideal way to achieve an autonomous and efficient operation of large-scale remote sensing satellite swarm. A distributed task allocation method based on the improved contract network algorithm is designed, orienting typical mission-level tasks. And the satellite swarm task allocation and planning model of potential target searching, moving target tracking, and sensitive target feature confirmation is given. The model is composed of observation requirement generation, observing area decomposition, and task allocation between different satellites. Simulation results confirm that the improved contract network algorithm can optimally solve the problem of mission-level task allocation autonomously in distributed swarm. This paper verifies that the self-organization method has the potential for engineering applications with simple realization theory and high calculation efficiency.