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An efficient iterative approximation model for low-thrust rendezvous in low-earth orbit is proposed to be applied in optimization of multi-debris removal missions. Based on an existing method for impulsive orbit rendezvous and a novel iteration process, the propellant cost and thrust time of a low-thrust rendezvous can be quickly evaluated. Moreover, another iterative method is also designed to help find the minimal transfer duration required for orbit rendezvous. Then, its application in the max-reward optimization of debris removal mission with a large-scale targets to be selected is studied. In the three-steps optimization including target selection, sequence optimization, and low-thrust trajectory optimization, the iterative approximation model is used to deal with the constraints and evaluate the objective functions more efficiently. The problem of the eighth Chinese trajectory optimization competition was used to test the performance, and a leading solution including 93 targets was found. Comparisons with previous results were made to prove the advantages of the method in this paper. [ABSTRACT FROM AUTHOR]

The design of multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal transfer between any two rendezvous targets. In this article, an artificial neural-network-based method is proposed for the rapid approximation of optimal perturbed long-duration impulsive transfers. The relationship between the optimal transfer velocity increments and the initial right ascension of the ascending node difference between the departure body and the rendezvous target is analyzed, and the result suggests that the perturbed long-duration impulsive transfers should be divided into three types. An efficient database generation method is developed. Three regression multilayer perceptrons (MLPs) are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained MLPs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all three types of transfers. Additional tests of the debris chains with total velocity increments of several thousand m/s show that the estimation results are very close to the optimized results, with a final estimation error of less than 10 m/s. [ABSTRACT FROM AUTHOR]

This paper proposes a warning threshold calculation method for instantaneous collision probability in a spacecraft rendezvous mission. By introducing missing alarm probability, the warning threshold is determined as a function of the propagated trajectory uncertainty and a semianalytical calculation model is presented. The simulation results show that the proposed method makes good use of trajectory uncertainty information and provides a reasonable and accurate method for evaluating the safety of a rendezvous trajectory with uncertainty. [ABSTRACT FROM AUTHOR]

This article studies the optimization of space station short-term mission planning (STMP) problems. The domain knowledge including description and the concept definitions of the STMP problem are presented, an STMP constraint satisfaction model is developed, and then an iterative conflict-repair method with the resolving strategies is proposed to satisfy complicated constraints. A genetic algorithm (GA) is adopted to optimize the STMP problem. The proposed approach is evaluated using a test case with 15 missions, 13 devices and three astronauts. The results show that the established STMP constraint satisfaction model is effective, and the iterative conflict-repair method can make the plan satisfy all constraints considered and can effectively improve the optimization performance of the GA. [ABSTRACT FROM AUTHOR]

Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers’ preferences. [ABSTRACT FROM AUTHOR]

Rendezvous and docking (RVD) is a key technology for performing complicated space missions. After an RVD process, several activities are executed to consolidate two docked spacecraft into a spacecraft complex, and this task phase is referred to as a spacecraft consolidation mission. It can save the mission time to execute these activities in parallel, but a high degree of parallelism could result in a disordered execution profile and many violations of precedence constraints. To solve this contradiction, a hybrid multi-objective optimisation approach is proposed. The precedence requirements within each activity are satisfied using an encoding and scheduling process, while the precedence requirements between different activities are treated by adding release time variables. A compact-execution index is designed to express the preference of an orderly and compact execution profile. Furthermore, a multi-objective hybrid-encoding genetic algorithm is employed to find optimal solutions. Finally, the proposed approach is demonstrated for a numerical example. The results show that optimal solutions satisfying precedence requirements both within each activity and between different activities are successfully obtained, and the trade-off between saving mission time and obtaining an orderly and compact execution profile can be effectively made. The performance of the proposed method is validated by comparison with two other multi-objective genetic algorithms. [ABSTRACT FROM PUBLISHER]

An optimization method which combines an indirect method with homotopic approach is proposed and applied to impulsive trajectories. Minimum-fuel, multiple-impulse solutions, with either fixed or open time are obtained. The homotopic approach at hand is relatively straightforward to implement and does not require an initial guess of adjoints, unlike previous adjoints estimation methods. A multiple-revolution Lambert solver is used to find multiple starting solutions for the homotopic procedure; this approach can guarantee to obtain multiple local solutions without relying on the user's intuition, thus efficiently exploring the solution space to find the global optimum. The indirect/homotopic approach proves to be quite effective and efficient in finding optimal solutions, and outperforms the joint use of evolutionary algorithms and deterministic methods in the test cases. [ABSTRACT FROM AUTHOR]

This study extends a previously proposed single-objective optimization formulation of space station logistics strategies to multi-objective optimization. The four-objective model seeks to maximize the mean utilization capacity index, total utilization capacity index, logistics robustness index and flight independency index, aiming to improve both the utilization benefit and the operational robustness of a space station operational scenario. Physical programming is employed to convert the four-objective optimization problem into a single-objective problem. A genetic algorithm is proposed to solve the resulting physical programming-based optimization problem. Moreover, the non-dominated sorting genetic algorithm-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the physical programming-based solution. The proposed approach is demonstrated with a notional one-year scenario of China's future space station. It is shown that the designer-preferred compromise solution improving both the utilization benefit and the operational robustness is successfully obtained. [ABSTRACT FROM AUTHOR]

The perturbed and constrained fuel-optimal impulsive rendezvous problem is formulated, developed, and solved. A non-linear programming model is established for the general perturbed fuel-optimal multiple-impulse time-fixed rendezvous with path constraints, and a hybrid approach is proposed as a global and efficient optimization tool. A floating-coded genetic algorithm is employed in this hybrid approach to locate an initial reference solution for sequential quadratic programming (SQP) using a simplified analytical propagator. Subsequently, SQP is used to locate the accurate solution using numerical integration of the high-fidelity trajectory dynamic equations. The hybrid approach is evaluated in three test cases: (i) Holman rendezvous and Lambert rendezvous, (ii) a three-impulse homing rendezvous with and without communication window constraints, and (iii) a four- and five-impulse non-coplanar multi-revolution rendezvous. The results show that the hybrid approach is effective and efficient in optimizing the perturbed and constrained fuel-optimal multiple-impulse rendezvous. [ABSTRACT FROM AUTHOR]

The effectiveness of simulated annealing (SA) to design near-optimal low-thrust trajectories is investigated. On the basis of SA, a global optimization approach is presented. This method has global convergence, and is also suitable for hybrid continuous and discrete variables. Moreover, new neighborhood search functions both for continuous variables and for discrete variables of SA are put forward. In order to reduce the computational cost, a hybrid approach combining SA with the Powell algorithm is also discussed. The minimum-time optimization and the minimum-fuel-consumption optimization of a low-thrust Earth-to-Mars orbit transfer are illustrated by SA. The results show that SA is superior to genetic algorithms and classical optimization algorithms. Reliable global convergence and high precision solution are guaranteed by the suggested SA, particularly for the hybrid SA algorithm. [ABSTRACT FROM AUTHOR]

Different from the current studies on the impulsive rendezvous which are mainly normal propellant-optimal or time-optimal designs, the safety-optimal impulsive rendezvous with consideration of practical trajectory uncertainties is investigated in this paper. An optimization model for safety-optimal rendezvous is established based on the Clohessy-Wiltshire (C-W) equations, which employs one new, recently proposed quantitative performance index of the rendezvous trajectory safety as the objective function. A real-coded genetic algorithm is adopted to locate the optimal solution. The safety index and the optimization approach are tested by several rendezvous problems with different numbers of impulses and different approach directions. The practical application value of the safety-optimal rendezvous trajectory is demonstrated by comparing with the traditional propellant-optimal and time-optimal rendezvous trajectories. [ABSTRACT FROM AUTHOR]