Automatic goal allocation for a planetary rover with DSmT

In this chapter, we propose an approach for assigning aninterest level to the goals of a planetary rover. Assigning an interest level to goals, allows the rover to autonomously transform and reallocate the goals. The interest level is defined by data-fusing payload and navigation information. The fusion yields an 'interest map',that quantifies the level of interest of each area around the rover. In this way the planner can choose the most interesting scientific objectives to be analysed, with limited human intervention, and reallocates its goals autonomously. The Dezert-Smarandache Theory of Plausible and Paradoxical Reasoning was used for information fusion: this theory allows dealing with vague and conflicting data. In particular, it allows us to directly model the behaviour of the scientists that have to evaluate the relevance of a particular set of goals. This chaptershows an application of the proposed approach to the generation of a reliable interest map

MGA trajectory planning with an ACO-inspired algorithm

Given a set of celestial bodies, the problem of finding an optimal sequence of gravity assist manoeuvres, deep space manoeuvres (DSM) and transfer arcs connecting two or more bodies in the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem, and its automated solution would greatly improve the assessment of multiple alternative mission options in a shorter time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the planetary sequence for a multiple gravity assist trajectory and a good estimation of the optimality of the associated trajectories. We propose the use of a two-dimensional trajectory model in which pairs of celestial bodies are connected by transfer arcs containing one DSM. The problem of matching the position of the planet at the time of arrival is solved by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. By using this model, for each departure date we can generate a full tree of possible transfers from departure to destination. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select one of the remaining feasible directions. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter, and solutions are compared to those found through traditional genetic-algorithm-based techniques

An ant system algorithm for automated trajectory planning

The paper presents an Ant System based algorithm to optimally plan multi-gravity assist trajectories. The algorithm is designed to solve planning problems in which there is a strong dependency of one decision one all the previously made decisions. In the case of multi-gravity assist trajectories planning, the number of possible paths grows exponentially with the number of planetary encounters. The proposed algorithm avoids scanning all the possible paths and provides good results at a low computational cost. The algorithm builds the solution incrementally, according to Ant System paradigms. Unlike standard ACO, at every planetary encounter, each ant makes a decision based on the information stored in a tabu and feasible list. The approach demonstrated to be competitive, on a number of instances of a real trajectory design problem, against known GA and PSO algorithms

Automatic MGA trajectory planning with a modified ant colony optimization algorithm

This paper assesses the problem of designing multiple gravity assist (MGA) trajectories, including the sequence of planetary encounters. The problem is treated as planning and scheduling of events, such that the original mixed combinatorial-continuous problem is discretised and converted into a purely discrete problem with a finite number of states. We propose the use of a two-dimensional trajectory model in which pairs of celestial bodies are connected by transfer arcs containing one deep-space manoeuvre. A modified Ant Colony Optimisation (ACO) algorithm is then used to look for the optimal solutions. This approach was applied to the design of optimal transfers to Saturn and to Mercury, and a comparison against standard genetic algorithm based optimisers shows its effectiveness

Orbit control of asteroids in libration point orbits for resource exploitation

The fascinating idea of shepherding asteroids for science and resource utilisation is being considered as a very credible concept in a not too distant future. Past studies have identified asteroids which could be injected into manifolds which wind onto periodic orbits around collinear Lagrangian points of the Sun-Earth system, by means of a low-cost manoeuvre. However, the periodic orbits as well as the manifolds are highly unstable, and small errors in the capture manoeuvre would bring to complete mission failure, with potential danger of collision with the Earth itself. The main source of injection error in position and velocity is the epistemic uncertainty of the asteroid mass, which cannot be measured directly. For this reason, asteroid orbit control will be a strict requirement for such mission. This paper investigates the controllability of some asteroids during the transfer and along the period orbits, assuming the use of a solar-electric low-thrust engine. The control scheme is based on a linear quadratic regulator. A stochastic simulation with a Monte Carlo approach is used to simulate a range of different perturbed initial conditions. Results show that only a small subset of the considered combinations of trajectories/asteroids are reliably controllable, and therefore controllability must be taken into account in the selection of potential targets

Coupled orbit and attitude dynamics of a reconfigurable spacecraft with solar radiation pressure

This work investigates the orbital and attitude dynamics of future reconfigurable multi-panel solar sails able to change their shape during a mission. This can be enabled either by changing the relative position of the individual panels, or by using articulated mechanisms and deployable, retractable and/or inflatable structures. Such a model introduces the concept of modular spacecraft of variable morphology to large gossamer spacecraft. However, this joint concept is complex in nature and requires equations for coupled orbit/attitude dynamics. Therefore, as a starting point, the system is modelled as a rigid-body dumbbell consisting of two tip masses connected by a rigid, massless panel. The system is subjected to a central gravitational force field under consideration of solar radiation pressure forces. Therefore, we assign reflectivity coefficients to the tip masses and a high area-to-mass ratio. An analytical Hamiltonian approach is used to describe the planar motion of the system in Sun-centred Keplerian and non-Keplerian circular orbits. The stability and controllability of the system is enabled through changing the reflectivity coefficients, for example through the use of electro-chromic coating on its surface. The creation of artificial unstable equilibria of the system due to the presence of solar radiation pressure and heteroclinic connections between the equilibria are investigated. We further derive a constraint for the solar radiation pressure forces to maintain the system on a circular Sun-centred orbit. It is planned that the structure is eventually capable of reconfiguring between the equilibria by a minimum actuation effort

Uncertainty modelling in reliable preliminary space mission design

In the early phase of the design of a space mission, it is generally desirable to investigate as many feasible alternative solutions as possible. At this particular stage, an insufficient consideration for uncertainty would lead to a wrong decision on the feasibility of the mission. Traditionally a system margin approach is used in order to take into account the inherent uncertainties within the subsystem budgets. The reliability of the mission is then independently computed in parallel. An iteration process between the solution design and the reliability assessment should finally converge to an acceptable solution. By combining modern statistical methods to model uncertainties and global search techniques for multidisciplinary design, the present work proposes a way to introduce uncertainties in the mission design problem formulation. By minimising the effect of these uncertainties on both constraints and objective functions, while optimising the mission goals, the aim is to increase the reliability of the produced results

Automated multigravity assist trajectory planning with a modified ant colony algorithm

The paper presents an approach to transcribe a multigravity assist trajectory design problem into an integrated planning and scheduling problem. A modified Ant Colony Optimization (ACO) algorithm is then used to generate optimal plans corresponding to optimal sequences of gravity assists and deep space manoeuvers to reach a given destination. The modified Ant Colony Algorithm is based on a hybridization between standard ACO paradigms and a tabu-based heuristic. The scheduling algorithm is integrated into the trajectory model to provide a fast time-allocation of the events along the trajectory. The approach demonstrated to be very effective on a number of real trajectory design problems

Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially when dealing with response properties of materials to e.g. electric fields, which require a good description of the perturbed electronic density. In this work, we use Gaussian process regression (GPR) to predict the static polarizability and dielectric susceptibility of molecules and molecular crystals. We combine this framework with ab initio molecular dynamics to predict their anharmonic vibrational Raman spectra. We stress the importance of data representation, symmetry, and locality, by comparing the performance of different flavors of GPR. In particular, we show the advantages of using a recently developed symmetry-adapted version of GPR. As an examplary application, we choose Paracetamol as an isolated molecule and in different crystal forms. We obtain accurate vibrational Raman spectra in all cases with fewer than 1000 training points, and obtain improvements when using a GPR trained on the molecular monomer as a baseline for the crystal GPR models. Finally, we show that our methodology is transferable across polymorphic forms: we can train the model on data for one structure, and still be able to accurately predict the spectrum for a second polymorph. This procedure provides an independent route to access electronic structure properties when performing force-evaluations on empirical force-fields or machine-learned potential energy surfaces

Shape-changing solar sails for novel mission applications

In order to increase the range of potential mission applications of solar sail technology, this paper introduces the concepts of shape change and continuously variable optical properties to large gossamer spacecraft. Merging the two concepts leads to the idea of solar sails as multi-functional platforms that can have potential benefits over conventional solar sails by delivering additional key mission functions such as power collection, sensing and communications. To this aim, the paper investigates the static deflection of a thin inelastic circular sail film with a variable surface reflectivity distribution. The sail film is modelled as a single surface framed by a rigid supporting hoop structure. When changing the reflectivity coefficient across the sail surface, the forces acting on the sail can be controlled without changing the incidence angle relative to the Sun. In addition, by assigning an appropriate reflectivity function across the sail, the load distribution due to solar radiation pressure can also be manipulated to control the billowing of the film. By an appropriate choice of reflectivity across the sail, specific geometries can be generated, such as a parabolic reflector, thus enabling a multi-functional sail. This novel concept of optical reconfiguration can potentially extend solar sail mission applications