A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation

A software platform for global optimisation, called PaGMO, has been developed within the Advanced Concepts Team (ACT) at the European Space Agency, and was recently released as an open-source project. PaGMO is built to tackle high-dimensional global optimisation problems, and it has been successfully used to find solutions to real-life engineering problems among which the preliminary design of interplanetary spacecraft trajectories - both chemical (including multiple flybys and deep-space maneuvers) and low-thrust (limited, at the moment, to single phase trajectories), the inverse design of nano-structured radiators and the design of non-reactive controllers for planetary rovers. Featuring an arsenal of global and local optimisation algorithms (including genetic algorithms, differential evolution, simulated annealing, particle swarm optimisation, compass search, improved harmony search, and various interfaces to libraries for local optimisation such as SNOPT, IPOPT, GSL and NLopt), PaGMO is at its core a C++ library which employs an object-oriented architecture providing a clean and easily-extensible optimisation framework. Adoption of multi-threaded programming ensures the efficient exploitation of modern multi-core architectures and allows for a straightforward implementation of the island model paradigm, in which multiple populations of candidate solutions asynchronously exchange information in order to speed-up and improve the optimisation process. In addition to the C++ interface, PaGMO's capabilities are exposed to the high-level language Python, so that it is possible to easily use PaGMO in an interactive session and take advantage of the numerous scientific Python libraries available.Comment: To be presented at 'ICATT 2010: International Conference on Astrodynamics Tools and Techniques

GTOC5: Results from the European Space Agency and University of Florence

http://www.esa.int/gsp/ACT/doc/ACTAFUTURA/AF08/papers/AF08.2014.45.pdfInternational audienc

Design of missions to the outer planets and optimization of low-thrust, gravity -assist trajectories via reduced parameterization

Three topics are discussed in the dissertation. The first topic addresses new ways of parameterizing the optimal control variables when maximizing the final spacecraft mass of low-thrust, gravity-assist trajectories. When the thrust is parameterized by on/off times (i.e. maximum thrust/coast arcs) and the steering angles are parameterized via Chebyshev series, the computational time can be reduced by an order of magnitude. The second topic is to design trajectories to the outer planets for a spacecraft using nuclear electric propulsion. Dozens of low-thrust, gravity-assist, rendezvous trajectories to Jupiter, Saturn, Uranus, Neptune, and Pluto are found. A rendezvous with Pluto via gravity assists with Earth and Jupiter requires a flight time of only 10 years for a propellant mass fraction of 50%. The final topic considers various end-of-life options for the Cassini mission, such as crashing the spacecraft into Saturn\u27s atmosphere. A Tisserand surface technique is developed that aids the design of gravity-assist trajectories that impact Saturn. Another encore scenario considers using gravity assists from Saturn\u27s largest moon, Titan, to eject Cassini from the Saturnian system to reach other gas giants

Multiple-target low-thrust interplanetary trajectory of DESTINY+

DESTINY+ is a medium-class interplanetary mission, selected by the Japan Aerospace Exploration Agency for potential launch windows in the first half of 2020s. The mission will demonstrate innovative spacecraft subsystem technologies, including a new type of ion engine for future missions. The mission will also collect scientific data through high-speed flyby observations and dust measurements from asteroid (3200) Phaethon and its related body (155140) 2005 UD, to understand their origin and reveal the content of extraterrestrial dust in the context of origin of life. The limited control authority on the spacecraft, the orbits of the target asteroids, and the specific mission requirements pose a challenging task for the trajectory design of DESTINY+. Multiple-target low-thrust optimal trajectories are explored in this paper to fulfill the goals of the DESTINY+ mission. An effective methodology is presented to convert feasible impulsive transfer solutions into low-thrust initial guesses and combine with gravity-assist maneuvers to reveal new high-fidelity optimal trajectories in real ephemeris models. The early mission analysis results demonstrate multitudes of flyby opportunities that provide robustness against programmatic and operational delays in the mission schedule

The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11

Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output. During the 11th edition of the GTOC challenge, participants were tasked with a complex trajectory planning related to the construction of a precursor Dyson structure, a heliocentric ring made of twelve stations. To this purpose, we developed several new approaches that synthesize techniques from machine learning, combinatorial optimization, planning and scheduling, and evolutionary optimization effectively integrated into a fully automated pipeline. These include a machine learned transfer time estimator, improving the established Edelbaum approximation and thus better informing a Lazy Race Tree Search to identify and collect asteroids with high arrival mass for the stations; a series of optimally-phased low-thrust transfers to all stations computed by indirect optimization techniques, exploiting the synodic periodicity of the system; and a modified Hungarian scheduling algorithm, which utilizes evolutionary techniques to arrange a mass-balanced arrival schedule out of all transfer possibilities. We describe the steps of our pipeline in detail with a special focus on how our approaches mutually benefit from each other. Lastly, we outline and analyze the final solution of our team, ACT&Friends, which ranked second at the GTOC 11 challenge