Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Abstract

This paper investigates the use of evolutionary algorithms for the optimization of time-constrained impulsive multirendezvous missions. The aim is to find the minimum- Δ V trajectory that allows a chaser spacecraft to perform, in a prescribed mission time, a complete tour of a set of targets, such as space debris or artificial satellites, which move on the same orbital plane at slightly different altitudes. For this purpose, a two-level design approach is pursued. First, an outer-level combinatorial problem is defined, dealing with the simultaneous optimization of the sequence of targets and the rendezvous epochs. The suggested approach is first tested by assuming that all transfer legs last exactly the same amount of time; then, the time domain is discretized over a finer grid, allowing a more appropriate sizing of the time window allocated for each leg. The outer-level problem is solved by an in-house genetic algorithm, which features an effective permutation-preserving solution encoding. A simple, but fairly accurate, heuristic, based on a suboptimal four-impulse analytic solution of the single-target rendezvous problem, is used when solving the combinatorial problem for a fast guess at the transfer cost, given the departure and arrival epochs. The outer-level problem solution is used to define an inner-level NLP problem, concerning the optimization of each body-to-body transfer leg. In this phase, the encounter times are further refined. The inner-level problem is tackled through an in-house multipopulation self-adaptive differential evolution algorithm. Numerical results for case studies including up to 20 targets with different time grids are presented.

A time-fuel hybrid optimal guidance for micro/nano satellite’s glideslope approach

“…Secondly, in the actual application scenario, the trajectory planning problem has strong nonlinearity and nonconvexity due to the consideration of the limitations of many constraints, which makes the solution of the optimal trajectory more complicated. Lastly, most of the current maneuvering planning models are single optimization objectives [6][7][8][9]; however, in practical tasks, space attack and defense, on-orbit maintenance, etc., the maneuvering planning algorithm should have the ability to comprehensively optimize multiple indicators such as solution time and fuel consumption to meet the requirements of the task.…”

“…Furthermore, the optimal burnup transfer trajectory is obtained by using the particle swarm optimization [6][7]. Federici et al proposed a bi-level optimization algorithm based on genetic algorithm and differential evolution algorithm for multi-impulse rendezvous tasks with time constraints and obtained the optimal burnup transfer trajectory [8]. For multi-objective trajectory optimization problems, the commonly used method is to convert them into single-objective optimization problems by setting weight coefficients [13].…”