Ant Colony Optimization-based Design of Multiple-target Active Debris Removal Mission

Zhang, Tianjiao; Shen, Hong-Xin; Yang, Yikang; Li, Hengnian; Li, Jisheng

doi:10.2322/tjsass.61.201

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Each element of the feasible solution and its probability constitute the discrete probability distribution of the ant sampling, so as to select the elements to be added to the current solution space C ij is the j-th element of the solution constructed by ant i, which is heuristic information and is generally chosen as the reciprocal of the objective function. The pheromone τ ij is updated as follows in Equation ( 24) [36]:…”

Section: Multiple Debris Selectionmentioning

confidence: 99%

Target Selection for a Space-Energy Driven Laser-Ablation Debris Removal System Based on Ant Colony Optimization

Yang

Shao

et al. 2023

Sustainability

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The space-energy driven laser-ablation debris removal technology can remove or detach multiple centimeter-level space debris in a single mission. However, the space-energy driven platform can only rely on its own equipment capabilities to detect and identify space debris. It is necessary to select multiple potentially removable debris targets to improve the removal efficiency. In this paper, target selection for a space-energy driven laser-ablation debris removal system is analyzed based on ant colony optimization. The intersection and interaction periods were given by the optimal driving sequence calculation for multiple debris. Parameters such as the detection range, pulsed energy, repetition frequency of the laser and trajectory of debris have been considered as inputs of the simulation. Target selection and optimal action time have been calculated when a single debris entered the detection range of the laser system. This optimization can significantly improve the overall efficiency and laser energy utilization of the space-based laser platform for the same randomly generated debris group, compared to the mode driven sequentially according to the order of entering the laser action range. The results showed that after being filtered by the ant colony algorithm, the number of removable debris doubled, and the de-orbit altitude increased by 15.9%. The energy utilization rate of the laser removal system has been improved by 74.6%. This optimization algorithm can significantly improve the overall work efficiency and laser energy utilization rate of the space-energy driven system. It can remove more debris or have a larger effective orbit reduction distance value for all debris.

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Section: Multiple Debris Selectionmentioning

confidence: 99%

Target Selection for a Space-Energy Driven Laser-Ablation Debris Removal System Based on Ant Colony Optimization

Yang

Shao

et al. 2023

Sustainability

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“…When searching for the best path and rendezvous times of each target in a multi-target sequence, the global optimization process needs to frequently evaluate the velocity increments of the transfers between the different targets with different flight times, which is extremely time-consuming. Most existing studies have employed different forms of approximation to improve the efficiency [5][6][7][8]11]. However, such a problem still lacks a solution that is fast enough for global optimization.…”

Section: Problem Description Of Orbit Rendezvousmentioning

confidence: 99%

“…To obtain efficient methods that quickly calculate the optimal velocity increment, several studies have focused on analytical methods based on dynamic approximations. A simple strategy is to calculate the orbit differences between the initial and target orbits and add them to the velocity increment separately [5,6]. It is fast enough, but cannot deal with the coupling terms between the different components of the orbit elements.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Approximation Model for Perturbed Orbit Rendezvous

Huang¹,

Wu²

2022

Mathematics

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An approximation of orbit rendezvous is usually used in the global optimization of multi-target rendezvous missions, which can greatly affect the efficiency of optimization process. A fast neural network-based surrogate model is proposed to approximate the optimal velocity increment of perturbed orbit rendezvous in low Earth orbits. According to a dynamic analysis, the initial and target orbits together with the flight time are transformed into a nine-dimensional normalized vector that is used as the input layer of the neural network. An existing approximation method is introduced to quickly generate the training data. In simulations, different numbers of layer nodes and hidden layers are tested to choose the best parameters. The proposed neural network model demonstrates high precision and high efficiency compared with previous approximation methods and neural network models. The mean relative error is less than 1%. Finally, a case of an optimization of a multi-target rendezvous mission is tested to prove the potential application of the neural network model.

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“…Most of these studies focus on long-term or time-free ADR missions, typically aimed at deorbiting the target debris at a rate of three to ten per year [4,5]. A typical mission strategy consists in exploiting the J 2 orbital perturbation for the alignment of the orbital planes of consecutive targets before starting the rendezvous maneuver in order to reduce the total mission cost [6,7], at the expense of a longer flight time. Conversely, when time-constrained or time-fixed missions are considered, the removal sequence and rendezvous epochs of the ADR mission must be optimized simultaneously, making the problem more complex to solve.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Federici

Zavoli

Colasurdo

2021

International Journal of Aerospace Engineering

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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.

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Ant Colony Optimization-based Design of Multiple-target Active Debris Removal Mission

Cited by 9 publications

References 16 publications

Target Selection for a Space-Energy Driven Laser-Ablation Debris Removal System Based on Ant Colony Optimization

Target Selection for a Space-Energy Driven Laser-Ablation Debris Removal System Based on Ant Colony Optimization

Neural Network-Based Approximation Model for Perturbed Orbit Rendezvous

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

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