Application of multi-agent coordination methods to the design of space debris mitigation tours

Stuart, Jeffrey; Howell, Kathleen C.; Wilson, Robyn

doi:10.1016/j.asr.2015.05.002

Cited by 31 publications

(7 citation statements)

References 18 publications

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“…A branch-and-prune strategy using low-thrust propulsion may be more efficient [65]. A multi-servicer approach invites ant colony optimisation routing and coordination through auction bidding [66]. In GEO orbit, the optimal schedule is to manoeuvre in an anti-orbit direction for minimum Δv [67].…”

Section: Space Debris Mitigationmentioning

confidence: 99%

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery

2019

Robotics

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Space-based manipulators have traditionally been tasked with robotic on-orbit servicing or assembly functions, but active debris removal has become a more urgent application. We present a much-needed tutorial review of many of the robotics aspects of active debris removal informed by activities in on-orbit servicing. We begin with a cursory review of on-orbit servicing manipulators followed by a short review on the space debris problem. Following brief consideration of the time delay problems in teleoperation, the meat of the paper explores the field of space robotics regarding the kinematics, dynamics and control of manipulators mounted onto spacecraft. The core of the issue concerns the spacecraft mounting which reacts in response to the motion of the manipulator. We favour the implementation of spacecraft attitude stabilisation to ease some of the computational issues that will become critical as increasing level of autonomy are implemented. We review issues concerned with physical manipulation and the problem of multiple arm operations. We conclude that space robotics is well-developed and sufficiently mature to tackling tasks such as active debris removal.

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Section: Space Debris Mitigationmentioning

confidence: 99%

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery

2019

Robotics

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“…Greedy heuristics, such as the Series Method [31], are among the fastest algorithms, but they can provide solutions quite far from the global optimum. Heuristic and metaheuristic treesearch procedures, such as Beam Search [32], Ant Colony Optimization (ACO) [33], or A * search [34], have been widely exploited for both ADR/OOS and asteroid exploration mission planning. A hybridization between Beam Search and ACO was also discussed [35].…”

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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“…Heuristic or meta-heuristic approaches are proposed for this high level transfer sequence optimization, such as exhaustive search [8], [16], [19], branch and bound [9], [13], [20], [25], Ant Colony Algorithm (ACO) [18], [26], Genetic Algorithm (GA) [2], [10], [11], [15] and heuristic greedy method [12], [14], [27]. However, for future autonomous spacecraft, the planning will be an action choosing procedure, which can produce the best actions constantly, and keep the exploring ability as well.…”

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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Application of multi-agent coordination methods to the design of space debris mitigation tours

Cited by 31 publications

References 18 publications

Tutorial Review on Space Manipulators for Space Debris Mitigation

Tutorial Review on Space Manipulators for Space Debris Mitigation

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

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

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