Low-thrust tour of the main belt asteroids

This work presents some preliminary results on the low-thrust tour of the main asteroid belt. The asteroids are visited through a series of fly-by’s that minimise the total cost of the manoeuvres. The sequence of asteroids to visit and the initial orbit for the spacecraft are chosen based on the Minimum Orbit Intersection Distance (MOID) between the orbit of the asteroids and the orbit of the spacecraft. The transfers between asteroids are designed using a low-thrust analytical model that provides nearly optimal solutions with coast and thrust arcs. The mission analysis is completed with a study of the transfer of the spacecraft from the Earth to the first orbit of the tour

Optimised GTO-GEO transfer using low-thrust propulsion

This paper proposes a global optimisation of the low-thrust transfers from GTO to GEO incorporating different types of perturbation. The trajectory transcription method makes use of an analytical solution of the perturbed Keplerian motion together with a simple direct collocation of the thrust arcs. The paper will show that low-thrust GTO to GEO transfers exhibit a number of local minima with a small but not negligible difference. The paper presents different strategies to explore the set of local minima and shows a number of locally optimal solutions

Multi-objective optimisation of constellation deployment using low-thrust propulsion

This work presents an analysis of the deployment of future constellations using a com-bination of low-thrust propulsion and natural dynamics. Different strategies to realise the transfer from the launcher injection orbit to the constellation operational orbit are investigated. The deployment of the constellation is formulated as a multi-objective optimisation problem that aims at minimising the maximum transfer ΔV, the launch cost and maximise at the same time the pay-off given by the service provided by the constellation. The paperwill consider the case of a typical constellation with 27 satellites in Medium Earth Orbit and the use of only two launchers, one of which can carry a single satellite. It will be demonstrated that some strategies and deployment sequences are dominant and provide the best trade-off between peak transfer ΔV and monetary pay-off

Multi-population inflationary differential evolution algorithm with adaptive local restart

In this paper a Multi-Population Inflationary Differential Evolution algorithm with Adaptive Local Restart is presented and extensively tested over more than fifty test functions from the CEC 2005, CEC 2011 and CEC 2014 competitions. The algorithm combines a multi-population adaptive Differential Evolution with local search and local and global restart procedures. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The local restart of the population, which follows the local search, is, therefore, automatically adapted

CAMELOT - computational-analytical multi-fidelity low-thrust optimisation toolbox

CAMELOT (Computational-Analytical Multi-fidelity Low-thrust Optimisation Toolbox) is a toolbox for the fast preliminary design and optimisation of low-thrust trajectories. It solves highly complex combinatorial problems to plan multi-target missions characterised by long spirals including different perturbations. In order to do so, CAMELOT implements a novel multi-fidelity approach combining analytical surrogate modelling and accurate computational estimations of the mission cost. Decisions are then made by using two pptimisation engines included in the toolbox, a single objective global optimiser and a combinatorial optimisation algorithm. CAMELOT has been applied to a variety of applications: from the design of interplanetary trajectories to the optimal deorbiting of space debris, from the deployment of constellations to on-orbit servicing. In this paper the main elements of CAMELOT are described and two space mission design problems solved using the toolbox are described

Automatic planning and scheduling of active removal of non-operational satellites in low earth orbit

In this paper two novel strategies to automatically design an optimized mission to de-orbit up to 10 non-cooperative objects per year are proposed, targeting the region within 800 and 1400 km altitude in LEO. The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches.The first strategy is analogous to the Traveling Salesman Problem: The servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that performs the re-entry. The second strategy is analogous to the Vehicle Routing Problem: The servicing spacecraft rendezvous with an object, spiral it down to a lower altitude orbit, and spiral up to the next target. In order to maximize the number of de-orbited non-operative objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimization algorithm. The incremental planning and scheduling algorithm uses a model based on optimal low-Thrust transfer between objects. The optimization of the transfers is realized using a direct method and an analytical propagator based on a first-order solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the J2 gravitational effect and the atmospheric drag

Automatic trajectory planning for low-thrust active removal mission in Low-Earth Orbit

In this paper two strategies are proposed to de-orbit up to 10 non-cooperative objects per year from the region within 800 and 1400 km altitude in Low Earth Orbit (LEO). The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches. The first strategy is analogous to the Traveling Salesman Problem: the servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that reduces the perigee of the orbit. The second strategy is analogous to the Vehicle Routing Problem: the servicing spacecraft rendezvous and docks with an object, spirals it down to a lower altitude orbit, undocks, and then spirals up to the next target. In order to maximise the number of de-orbited objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimisation algorithm. The optimisation of the resulting sequence is realised using a direct transcription method based on an asymptotic analytical solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the $J_2$ gravitational effect and the atmospheric drag

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

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

Classifying intelligence in machines : a taxonomy of intelligent control

The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent

Earth-Mars transfers through Moon distant retrograde orbits

This paper focuses on trajectory design which is relevant for missions that would follow NASA’s Asteroid Redirect Mission (ARM) to further explore and utilise asteroids and eventually human Mars exploration. Assuming that a refueling gas station is present at a given Lunar Distant Retrograde Orbit (DRO), we analyse ways of departing from the Earth to Mars via that DRO. Thus, the analysis and results presented in this paper add a new cis-lunar departure orbit for Earth-Mars missions. Porkchop plots depicting the required C3 at launch, v1 at arrival, Time of Flight (TOF), and total ∆V for various DRO departure and Mars arrival dates are created and compared with results obtained for low ∆V LEO to Mars trajectories. The results show that low ∆V DRO to Mars transfers generally have lower ∆V and TOF than LEO to Mars maneuvers