Mission planning for on-orbit servicing through multiple servicing satellites: A new approach

Daneshjou, K.; Mohammadi-Dehabadi, A.A.; Bakhtiari, Majid

doi:10.1016/j.asr.2017.05.037

Cited by 51 publications

(22 citation statements)

References 59 publications

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“…The optimal planning of OOS missions in the geosynchronous orbit was also investigated in several research papers; the same approach for the preliminary design of ADR mission, with a single active chaser aimed at multiple passive targets, can be also found in several works about OOS [12]. However, nonstandard, and even more complex, mission scenarios become more frequent in OOS literature, encompassing the presence of multiple servicing spacecraft [13], cooperative targets [14], a fuel depot [15], constellation reconfiguration maneuvers [16], or complex dynamical models [17]. As for ADR missions, the great part of the studies about OOS deals with small constellations of satellites or time-free missions, in order to reduce the problem complexity.…”

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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Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Federici

Zavoli

Colasurdo

2021

International Journal of Aerospace Engineering

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“…Therefore, protecting the safety of space assets in face of these threats is critical for cleaning space. Many studies have been done to find a way out, e.g., space situational awareness, on-orbit servicing and so on [5][6][7][8][9][10][11][12]. Developing on-orbit servicing vehicles with corresponding GNC (guidance, navigation and control) systems used to handle the space debris, inactive satellites and military vehicles threating to the space asset is the most effective method for safety.…”

Section: Introductionmentioning

confidence: 99%

Incomplete Information Pursuit-Evasion Game Control for a Space Non-Cooperative Target

et al. 2021

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Aiming to solve the optimal control problem for the pursuit-evasion game with a space non-cooperative target under the condition of incomplete information, a new method degenerating the game into a strong tracking problem is proposed, where the unknown target maneuver is processed as colored noise. First, the relative motion is modeled in the rotating local vertical local horizontal (LVLH) frame originated at a virtual Chief based on the Hill-Clohessy-Wiltshire relative dynamics, while the measurement models for three different sensor schemes (i.e., single LOS (line-of-sight) sensor, LOS range sensor and double LOS sensor) are established and an extended Kalman Filter (EKF) is used to obtain the relative state of target. Next, under the assumption that the unknown maneuver of the target is colored noise, the game control law of chaser is derived based on the linear quadratic differential game theory. Furthermore, the optimal control law considering the thrust limitation is obtained. After that, the observability of the relative orbit state is analyzed, where the relative orbit is weakly observable in a short period of time in the case of only LOS angle measurements, fully observable in the cases of LOS range and double LOS measurement schemes. Finally, numerical simulations are conducted to verify the proposed method. The results show that by using the single LOS scheme, the chaser would firstly approach the target but then would lose the game because of the existence of the target’s unknown maneuver. Conversely, the chaser can successfully win the game in the cases of LOS range and double LOS sensor schemes.

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“…The literature related to assignment optimization and the deployments of OSVs are as follows. A novel approach for the mission planning of on-orbit servicing such as visual inspection is proposed by Daneshjou et al [9]. The on-orbit servicing task allocation is solved by a hybrid discrete particle swarm optimization algorithm which is proposed by Zhang and Zhang [10].…”

Section: Introductionmentioning

confidence: 99%

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Xing-long

Liu

et al. 2021

Modelling and Simulation in Engineering

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On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.

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Mission planning for on-orbit servicing through multiple servicing satellites: A new approach

Cited by 51 publications

References 59 publications

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Incomplete Information Pursuit-Evasion Game Control for a Space Non-Cooperative Target

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

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