A multidirectional Physarum solver for the automated design of space trajectories

Masi, Luca; Vasile, Massimiliano

doi:10.1109/cec.2014.6900287

Cited by 9 publications

(11 citation statements)

References 20 publications

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“…In analogy with A n type of path planning or with dynamic programming algorithms, when the search proceeds forward from the source to the sink, the backward branches work as the heuristic function and vice versa when the search proceeds backward. The algorithm has already been extensively tested on a variety of known Travelling Salesman and Vehicle Routing problems with good results [20,21].…”

Section: Multi-directional Discrete Decision Makingmentioning

confidence: 99%

Incremental planning of multi-gravity assist trajectories

Vasile¹,

Martin²,

Masi³

et al. 2015

Acta Astronautica

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The paper presents a novel algorithm for the automatic planning and scheduling of multi-gravity assist trajectories (MGA). The algorithm translates the design of a MGA transfer into a planning and scheduling process in which each planetary encounter is seen as a scheduled task. All possible transfers form a directional graph that is incrementally built and explored simultaneously forward from the departure planet to the arrival one and backward from the arrival planet to the departure one. Nodes in the graph (or tree) represent tasks (or planetary encounters). Backward and forward generated transfers are then matched during the construction of the tree to improve both convergence and exploration. It can be shown, in fact, that the multi-directional exploration of the tree allows for better quality solutions for the same computational cost. Unlike branch and prune algorithms that use a set of deterministic branching and pruning heuristics, the algorithm proposed in this paper progressively builds a probabilistic model over all the possible tasks that form a complete trajectory. No branch is pruned but the probability of selecting one particular task increases as the algorithm progresses in the search for a solution. The effectiveness of the algorithm is demonstrated on the design optimization of the trajectory of Marco Polo, JUICE and MESSENGER missions

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Section: Multi-directional Discrete Decision Makingmentioning

confidence: 99%

Incremental planning of multi-gravity assist trajectories

Vasile¹,

Martin²,

Masi³

et al. 2015

Acta Astronautica

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“…Discrete decision making problems are common problems within the field of engineering. For example, the design of a network, the scheduling of operations and the planning of a trajectory can be represented as combinatorial problems [1] [2] [3]. As the complexity of such problems increases with the growth of the set of decisions, it is favourable to develop efficient strategies to solve these problems.…”

Section: Introductionmentioning

confidence: 99%

Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm

Vroom

Carlo

Martin

et al. 2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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In this paper, a combinatorial optimisation algorithm inspired by the Physarum Polycephalum mould is presented and applied to the optimal trajectory planning of a multiple asteroid tour mission. The Automatic Incremental Decision Making And Planning (AIDMAP) algorithm is capable of solving complex discrete decision making problems with the use of the growth and exploration of the decision network. The stochastic AIDMAP algorithm has been tested on two discrete astrodynamic decision making problems of increased complexity and compared in terms of accuracy and computational cost to its deterministic counterpart. The results obtained for a mission to the Atira asteroids and to the Main Asteroid Belt show that this non-deterministic algorithm is a good alternative to the use of traditional deterministic combinatorial solvers, as the computational cost scales better with the complexity of the problem

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“…AIDMAP has been extensively tested on a variety of Travelling Salesman and Vehicle Routing problems, providing good results [7,19,27].…”

Section: Aidmapmentioning

confidence: 99%

CAMELOT: Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox

2017

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A multidirectional Physarum solver for the automated design of space trajectories

Cited by 9 publications

References 20 publications

Incremental planning of multi-gravity assist trajectories

Incremental planning of multi-gravity assist trajectories

Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm

CAMELOT: Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox

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