A Homotopy-Based Method for Optimization of Hybrid High-Low Thrust Trajectories

Space missions require increasingly more efficient trajectories to provide payload transport and mission goals by means of lowest fuel consumption, a strategic mission design key-point. Recent works demonstrated that the combined (or hybrid) use of chemical and electrical propulsion can give important advantages in terms of fuel consumption, without losing the ability to reach other mission objectives: as an example the Hohmann Spiral Transfer, applied in the case of a transfer to GEO orbit, demonstrated a fuel mass saving between 5-10% of the spacecraft wet mass, whilst satisfying a pre-set boundary constraint for the time of flight. Nevertheless, methods specifically developed for optimizing space trajectories considering the use of hybrid high-low thrust propulsion systems have not been extensively developed, basically because of the intrinsic complexity in the solution of optimal problem equations with existent numerical methods. The study undertaken and presented in this paper develops a numerical strategy for the optimization of hybrid high-low thrust space trajectories. An indirect optimization method has been developed, which makes use of a homotopic approach for numerical convergence improvement. The adoption of a homotopic approach provides a relaxation to the optimal problem, transforming it into a simplest problem to solve in which the optimal problem presents smoother equations and the shooting function acquires an increased convergence radius: the original optimal problem is then reached through a homotopy parameter continuation. Moreover, the use of homotopy can make possible to include a high thrust impulse (treated as velocity discontinuity) to the low thrust optimal control obtained from the indirect method. The impulse magnitude, location and direction are obtained following from a numerical continuation in order to minimize the problem cost function. The initial study carried out in this paper is finally correlated with particular test cases, in order to validate the work developed and to start investigating in which cases the effectiveness of hybrid-thrust propulsion subsists

Orbital Dynamics of an Oscillating Sail in the Earth-Moon System

The oscillating sail is a novel solar sail configuration where a triangular sail is released at a deflected angle with respect to the Sun-direction. As a result, the sail will conduct an undamped oscillating motion around the Sun-line due to the offset between the centre-of-pressure and centre-of-mass. In this paper, the resulting oscillatory motion of the acceleration vector is exploited to design new families of periodic orbits in the Earth-Moon circular restricted three-body system. In particular, the effect of adding an oscillating sail to the family of Lyapunov orbits at the L1- and L2-points as well as the family of distant retrograde orbits (DROs) is investigated. Because the solar sail Earth-Moon system is non-autonomous (due to the apparent orbital motion of the Sun), the sail’s oscillating period, the orbital period and the period of the Sun around the Earth-Moon system all need to be commensurable in order for the orbits to be repeatable over time. Using a differential correction technique, orbits that satisfy these constraints can be obtained and the results comprise new families of periodic orbits that are parameterised by the required sail performance. In addition to exploiting the oscillating sail for generating new orbit families, this paper also investigates its potential for orbital transfers. By combining a systematic search method with a local optimiser, oscillating sail parameters and orbital parameters can be obtained that enable transfers between classical Lyapunov orbits at the L1-point, connections between classical Lyapunov orbits at different Lagrange points as well as transfers between orbits within the family of classical DROs

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

Hybrid solar sail and SEP propulsion for novel Earth observation missions

In this paper we propose a pole-sitter spacecraft hybridising solar electric propulsion (SEP) and solar sailing. The intriguing concept of a hybrid propulsion spacecraft is attractive: by combining the two forms of propulsion, the drawbacks of the two systems cancel each other, potentially enabling propellant mass saving, increased reliability, versatility and lifetime over the two independent systems. This almost completely unexplored concept will be applied to the continuous monitoring of the Earth’s polar regions through a pole-sitter, i.e. a spacecraft that is stationary above one pole of the Earth. The continuous, hemispherical, real-time view of the pole will enable a wide range of new applications for Earth observation and telecommunications. In this paper, families of 1-year-periodic, minimum-propellant orbits are found, for different values of the sail lightness number and distance from the pole. The optimal control problem is solved using a pseudo-spectral method. The process gives a reference control to maintain these orbits. In addition, for stability issues, a feedback control is designed to guarantee station-keeping in the presence of injection errors, sail degradation and temporary SEP failure. Results show that propellant mass can be saved by using a medium-sized solar sail. Finally, it is shown that the feedback control is able to maintain the spacecraft on-track with only minimal additional effort from the SEP thruster

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

Computing the Absolute Gibbs Free Energy in Atomistic Simulations: Applications to Defects in Solids

The Gibbs free energy is the fundamental thermodynamic potential underlying the relative stability of different states of matter under constant-pressure conditions. However, computing this quantity from atomic-scale simulations is far from trivial. As a consequence, all too often the potential energy of the system is used as a proxy, overlooking entropic and anharmonic effects. Here we discuss a combination of different thermodynamic integration routes to obtain the absolute Gibbs free energy of a solid system starting from a harmonic reference state. This approach enables the direct comparison between the free energy of different structures, circumventing the need to sample the transition paths between them. We showcase this thermodynamic integration scheme by computing the Gibbs free energy associated with a vacancy in BCC iron, and the intrinsic stacking fault free energy of nickel. These examples highlight the pitfalls of estimating the free energy of crystallographic defects only using the minimum potential energy, which overestimates the vacancy free energy by 60% and the stacking-fault energy by almost 300% at temperatures close to the melting point

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

Optimization of intersatellite routing for real-time data download

The objective of this study is to develop a strategy to maximise the available bandwidth to Earth of a satellite constellation through inter-satellite links. Optimal signal routing is achieved by mimicking the way in which ant colonies locate food sources, where the 'ants' are explorative data packets aiming to find a near-optimal route to Earth. Demonstrating the method on a case-study of a space weather monitoring constellation; we show the real-time downloadable rate to Earth

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