Autonomous Mission Planning for Spacecraft On-orbit Service Based on Hybrid Variant Particle Swarm Optimization

Abstract

For the complex on-orbit service (OOS) mission planning problem, an improved hybrid variant particle swarm optimization (PSO) method was proposed to improve the fuel utilization efficiency of spacecraft. Firstly, considering the time and fuel constraints, a model for OOS mission planning under the scenario of multiple target spacecrafts is established. The Lambert orbit maneuver method is used and the problem is transformed into a large-scale hybrid nonlinear integer programming problem. Next, an improved hybrid variant PSO algorithm is designed to optimize the fuel performance cost, and its simple structure and lightweight operation facilitate autonomous planning, considering the limited computing power of the onboard computer. The hybrid algorithm is composed of discrete particle swarm optimization (DPSO) and PSO, so it has a strong ability to search for the optimal service sequence and orbit maneuver time simultaneously. Moreover, the adaptive and variant operators are used to improve the searching ability and avoid falling into local optimal solutions. Finally, simulation experiments show that the proposed method can quickly give optimal results, reducing fuel consumption effectively in the OOS mission and improving the OOS capability of the spacecraft.