Mission Planning of GEO Active Debris Removal Based on Revolver Mode

Abstract

The mission planning of active debris removal (ADR) of revolver mode on geosynchronous orbit (GEO) is studied in this paper. It is assumed that there are one service satellite, one space depot, and some pieces of space debris in the ADR mission. The service satellite firstly rendezvouses with the debris and then releases the thruster deorbit kits (TDKs), which are carried with the satellite, to push the debris to the graveyard orbit. Space depot will provide replenishment for the service satellite. The purpose of this mission planning is to optimize the ADR sequence of the service satellite, which represents the chronological order, in which the service satellite approaches different debris. In this paper, the mission cost will be stated firstly, and then a mathematical optimization model is proposed. ADR sequence and orbital transfer time are used as designed variables, whereas the fuel consumption in the whole mission is regarded as objective for optimizing, and a specific number of TDKs is also a new constraint. Then, two-level optimization is proposed to solve the mission planning problem, which is low-level for finding optimal transfer orbit using accelerated particle swarm optimization (APSO) algorithm and up-level for finding best mission sequence using immune genetic (IGA) algorithm. Numerical simulations are carried out to demonstrate the effectiveness of the model and the optimization method. Results show that TDK number influences the fuel consumption through impacting the replenishing frequency and TDK redundancy. To reduce fuel consumption, the TDK number should be optimized and designed with suitable replenishing frequency and minimum TDK redundancy.

Large-scale mission scheduling for multiple heterogeneous agile earth observation satellites using modified chameleon swarm algorithm

“…Metaheuristic algorithms are widely used in solving large-scale multi-satellites mission scheduling problems. Classical metaheuristic algorithms include but are not limited to genetic algorithm (GA) [24][25][26], particle swarm optimization (PSO) [27][28][29], ant colony optimization (ACO) [30][31], artificial bee colony algorithm (ABC) [32][33], tabu search (TS) [34][35]and simulated annealing (SA) [36][37][38].…”