Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions

Abstract

This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, development of an approach and obtaining the solution of the problem. Several subcategories for each step have been identified and described. Subsequently, important classifications and their characteristics have been discussed for solving the problems. Finally, a discussion on how to choose an element of each step for a given problem is provided.

A Reinforcement Learning Scheme for Active Multi-Debris Removal Mission Planning With Modified Upper Confidence Bound Tree Search

“…(3), the inclination change is achieved via a velocity impulse at the nodes of initial orbit or target orbit, and the RAAN is changed on drift orbit with the natural procession of J 2 zonal term. The impulse cost C v is accessed through the cost function [11] which can be established by the trajectory optimization methods in [42]. The nonlinear programming…”

An evolutionary discretized Lambert approach for optimal long-range rendezvous considering impulse limit

“…When comparing one metaheuristic to another in a spacecraft trajectory optimization problem, it is crucial to perform benchmark tests using a suite of standard problems [10]. In order to make a practical comparison between algorithms, it is important to consider several factors.…”

“…One effective option to deal with this difficulty is to use meta-heuristics and evolutionary algorithms [8,9]. As a result, a more thorough investigation is needed to find the best solution using these numerical methods and techniques [10].…”

A new shape-based multiple-impulse strategy for coplanar orbital maneuvers

“…In addition, the effect of path and thrust constraints on impulsive maneuvers have been investigated [5,6], and the optimal impulsive transfer in presence of time constraints is driven [7]. Further, various heuristic optimization algorithms such as the genetic algorithm (GA) [8], the particle swarm optimization (PSO) [9,10], and the simulated annealing [11] have been proposed to design an optimal impulsive trajectory (also the reader can refer to [12] and references therein for more details). However, the Lamberts approach has traditionally been utilized for conic trajectories between any two spatial points in space within a specified time interval.…”