Research on Optimal Mission Planning for Satellite Swarm Configuration Change

This study addresses the challenges of mission allocation and real-time path planning for satellite swarms under strict fuel constraints and computational complexity limitations. By leveraging the Clohessy-Wiltshire (CW) guidance equations, a fuel consumption model is established to quantify propellant expenditure, with optimization objectives targeting both total fuel minimization and fuel equity across the swarm. A particle swarm optimization (PSO) algorithm transforms task allocation into a constrained programming problem, enabling efficient resource distribution for long-term orbital services. To overcome the scalability limitations of centralized methods, a distributed sequential convex programming (SCP) framework is proposed, allowing individual spacecraft to autonomously compute fuel-optimal trajectories while rigorously satisfying orbital dynamics, collision avoidance, and fuel consumption constraints. This dual-layered approach reduces system-wide computational complexity by localizing optimization tasks, achieving the scalability for real-time operations in large-scale swarms, thereby advancing sustainable mission planning capabilities for next-generation space systems.