Mission Planning Modeling And Optimization For On-Orbit Servicing In The Geosynchronous Earth Orbit

On-orbit servicing for geosynchronous Earth orbit(GEO)is able to reduce the loss of satellites breakdown,extend the useful life of satellites and protect GEO resources effectively.GEO on-orbit servicing and its relevant technology have become an important research direction in the field of aerospace due to the remarkable benefit.Mission planning optimization for GEO on-orbit servicing is to work out the best mission scheme,in order to accomplish the task successfully.It includes determining the deployment and number of servicing satellites,task assignment,servicing order,transfer orbit,mission time distribution and so on,satisfying all kinds of complicated constraints.The planning of GEO on-orbit servicing missions studied in this dissertation is a multi-variable,multi-objective,multi-constraints problem,with a complicated model and difficult to solve.This dissertation proposes three mission scenarios,including GEO targets approaching visual inspection,on-orbit refueling and active GEO debris removal,based on which mission planning models are built,solving frameworks and optimization algorithms are designed.The main results achieved in this dissertation are listed as follows.The mathematic models of three classes of GEO on-orbit servicing mission planning are established.Based on the analysis of GEO targets approaching visual inspection,on-orbit refueling and active GEO debris removal mission planning optimization problem,the common sub-problems are summarized,namely routing problem,trajectory optimization problem and location problem of servicing satellites.Based on the travelling salesman problem and the vehicle routing problem,the mathematic models of three on-orbit servicing mission planning problems are established.A planning approach is developed for multiple GEO satellites approaching inspection mission.A new location and maneuver strategy of servicing satellite is proposed,which is propellant saving and easy to implement in practice.Each servicing satellite is placed in an equatorial high eccentric orbit initially.Two orbital maneuvers are exerted at perigee to adjust the apogee of the servicing satellite for every inspection.Subsequently,the GEO targets will be visited when they fly through the ascending or descending nodes of their orbits.Two-nested optimization models for “one-to-many” and “many-to-many” GEO satellites approaching inspection mission are built,respectively.For the “one-to-many” mission planning problem,genetic algorithm and a rapid search method are applied to optimize the visiting order and visiting time of GEO targets,transfer trajectory of servicing satellite.For the “many-to-many” mission planning problem,a two-nested hybrid solving approach is developed based on the genetic algorithm and branch and bound algorithm.The design variables of outer-loop problem are the locations of servicing satellites,task assignment and visiting order.In the inner-loop problem,the transfer trajectory of servicing satellite is optimized,including the visiting time of GEO targets and the maneuver schemes of serving satellites.Numerical simulations demonstrate the effectiveness of the proposed methodology.A single objective planning approach is proposed for multiple GEO satellites refueling mission.A different refueling type is proposed,in which servicing satellite and fuel station are used to accomplish the multiple GEO satellites refueling mission.Servicing satellite transfers between GEO targets and the fuel station,delivering the fuel stored in the fuel station to the GEO targets.For the single objective planning problem of on-orbit refueling mission,two optimization models are built for “one-to-many” and “many-to-many” refueling manner.A solving approach based on genetic algorithm and random search algorithm is developed to deal with the “one-to-many” mission planning problem,optimizing the route of the servicing satellite.Particle swarm optimization algorithm and the exhaustive search are employed to solve the “many-to-many” mission planning problem,optimizing mission distribution and the route of the servicing satellites.The results show that the optimal routing of each servicing satellite can be obtained successfully.A multi-objective planning approach is proposed for multiple GEO satellites refueling mission.The two-nested multi-objective optimization model is built for “one-to-many” multiple geostationary satellites refueling mission.The hybrid approach based on NSGA-II and branch and bound algorithm are designed to optimize the visiting routine and time of GEO targets and the fuel station,and the transfer trajectory of the servicing satellite.The two-nested multi-objective optimization model is built for “many-to-many” multiple GEO satellites refueling mission.The hybrid approach based on a new multi-objective hybrid particle swarm optimization and branch and bound algorithm are developed to optimize the locations of fuel stations,task assignment,the routine and time of servicing satellites for visiting GEO targets and fuel stations,and the transfer trajectory of the servicing satellites.Simulation results show that the proposed is capable of obtaining a set of Pareto optimal solutions,indicating the tradeoff relationships between the mission time and the propellant consumption.A planning approach is proposed for multiple GEO debris removal mission.Servicing satellite and fuel station are used to accomplish the active GEO debris removal mission,and the removal procedure is presented in detail.Ant colony algorithm is designed to solve the planning problems of debris removal mission with a servicing satellite,a servicing satellite and a fuel station,multiple servicing satellites and fuel stations,and the parameters of ant colony algorithm are automatically tuned by the iterated F-Race method.Numerical simulations are presented to illustrate that for the debris removal mission,the ant colony algorithm performs better than the genetic algorithm and the simulated annealing algorithm.