Optimal planning of LEO active debris removal based on hybrid optimal control theory

Yu, Jian; Chen, Xiaoqian; Li-hu, Chen

doi:10.1016/j.asr.2015.02.026

Cited by 42 publications

(13 citation statements)

References 23 publications

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“…Papers containing examples in which the flyby problem of LSD objects is solved were published only in international journals in English. For example, the authors of [15] considered the first deorbiting scheme, proposing that the orbits of selected LSD objects would be located approximately in the same plane. Paper [16] considered a satellite system of 38 vehicles at noncoplanar orbits.…”

Section: Example Of Calculating Flyby Maneuvers For Groupmentioning

confidence: 99%

“…Taking into account the real spatial distribution of LSD objects in all five distinguished groups (each group contains only a few objects whose orbits deviate in the RAAN and with an inclination close to zero), the formulation of the problem and, accordingly, its solution offered in [15] are not applicable to real LSD objects.…”

Section: Example Of Calculating Flyby Maneuvers For Groupmentioning

confidence: 99%

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Solution of the flyby problem for large space debris at sun-synchronous orbits

et al. 2016

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the paper considers the flyby problem related to large space debris (LSD) objects at low earth orbits. The data on the overall dimensions of known last and upper stages of launch vehicles makes it possible to single out five compact groups of such objects from the NORAD catalog in the 500-2000 km altitude interval. The orbits of objects of each group have approximately the same inclinations. The features of the mutual distribution of the orbital planes of LSD objects in the group are shown in a portrait of the evolution of deviations of the right ascension of ascending nodes (RAAN). In the case of the first three groups (inclinations of 71°, 74°, and 81°), the straight lines of relative RAAN deviations of object orbits barely intersect each other. The fourth (83°) and fifth (97°-100°) LSD groups include a considerable number of objects whose orbits are described by straight lines (diagonals), which intersect other lines many times. The use of diagonals makes it possible to significantly reduce the temporal and total characteristic velocity expenditures required for object flybys, but it complicates determination of the flyby sequence. Diagonal solutions can be obtained using elements of graph theory. A solution to the flyby problem is presented for the case of group 5, formed of LSD objects at sun-synchronous orbits.

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Section: Example Of Calculating Flyby Maneuvers For Groupmentioning

confidence: 99%

Section: Example Of Calculating Flyby Maneuvers For Groupmentioning

confidence: 99%

Solution of the flyby problem for large space debris at sun-synchronous orbits

et al. 2016

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“…As the ADR mission planning technique is developed from 2010s, most researchers minimized ∆v cost of fixed length of removal sequence under particular dynamic model assumptions, such as Barbee et al [14] is under co-elliptic rendezvous strategy, Cerf [13], [15] is under drift-orbit transfer strategy with J 2 perturbation considered, Braun et al [8] is under four different scenarios with J 2 perturbation considered, Yu te al. [16], [17] is under two-impulse rendezvous, Shen et al [18] is under four-impulse transfer strategy with J 2 perturbation considered. Meanwhile, some researches also minimized the total mission time additionally [9], [17], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

“…[16], [17] is under two-impulse rendezvous, Shen et al [18] is under four-impulse transfer strategy with J 2 perturbation considered. Meanwhile, some researches also minimized the total mission time additionally [9], [17], [19], [20]. As the performance is a more practical concern about space missions, Liu et al [10], [11], Zhao et al [21], and Carlo et al [22] added the reward of debris as one of the objectives under J 2 perturbation [10], [11], [22] and under atmospheric drag perturbation [22].…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Hou

et al. 2020

IEEE Access

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The increasing number of space debris is a critical impact on space environment. Active multi-debris removal (ADR) mission planning technique with maximal reward objective is getting more attention. As the goal of Reinforcement Learning (RL) is in accordance with maximal-reward optimization model of ADR, the planning will be more efficient with the advanced RL scheme and RL algorithm. In this paper, first, an RL formulation is presented for the ADR mission planning problem. All the basic components of maximal-reward optimization model are recast in RL scheme. Second, a modified Upper Confidence bound Tree (UCT) search algorithm for the ADR planning task is developed, which both leverages the neural-network-assisted selection and expansion procedures for a better exploration and uses roll-out simulation in the backup procedure for robust value estimation. This algorithm fits the RL scheme of ADR mission planning and better balances the exploration and exploitation. Experimental comparison using three subsets of Iridium 33 debris cloud data reveals a better performance of this modified UCT over previously reported results and close UCT variants. INDEX TERMS Monte Carlo tree search, multi-debris active removal, reinforcement learning, space mission planning

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“…Recently, hybrid optimal control (HOC) theory has been applied to the solution of space mission planning [11][12][13][14][15][16][17]. The common way to model a HOC problem consists of the steps of categorical state space modelling; continuous-time dynamics modelling; continuous-valued state and control spaces modeling; discrete events modelling; and cost functions modelling.…”

Section: Mathematical Description Of the Hoc Problemmentioning

confidence: 99%

Optimal Scheduling of GEO On-orbit Refuelling with Uncertain Object Satellites

Huang

et al. 2017

MATEC Web Conf.

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Abstract. The mission planning of Geostationary Earth Orbit (GEO) on-orbit refuelling with uncertain object satellites is studied in this paper. Three key problems should be resolved here: Object Satellite (OSc) selection, mission sequence planning and transfer trajectory optimization. The optimization goal is to select the OSc and find the optimal servicing sequence with the best optimization objective value. Three optimization objectives, i.e. fuel cost, the total number of the OScs and the sum of the OSc priority value are taken into account. Considering this mission as a hybrid optimal control problem, a mathematical model is proposed. Multiobjective particle swarm optimization is employed to address the model. Several mission scenarios are compared and discussed. Numerical experiments indicate that 1) the method developed in this paper could address the problem efficiently; and 2) OSc distribution and varieties in optimization objective emphasis have considerable influences on the optimization results.

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Optimal planning of LEO active debris removal based on hybrid optimal control theory

Cited by 42 publications

References 23 publications

Solution of the flyby problem for large space debris at sun-synchronous orbits

Solution of the flyby problem for large space debris at sun-synchronous orbits

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

Optimal Scheduling of GEO On-orbit Refuelling with Uncertain Object Satellites

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