Asteroid belt multiple flyby options for M-Class Missions

Addressing many of the fundamental questions in modern planetary science, as well as in ESA’s cosmic vision, requires a comprehensive knowledge of our Solar System’s asteroid belt. This paper investigates potential opportunities for medium-class asteroid belt survey missions in the timeframe of 2029-2030. The study has been developed in support to CASTAway Asteroid Spectroscopic Survey mission proposal, which is to be submitted to the latest ESA’s medium size mission call. CASTAway envisages the launch of a small telescope with relatively straightforward (i.e. high TRL) remote sensing instrumentation to observe asteroids at a long-range (i.e. point source), but also at a short-range, resolving them at ~10 m resolution. This paper presents a challenging multi-objective optimization problem and discusses the feasibility of such a mission concept. A baseline trajectory is presented that meets both ESA’s medium size mission constraints and the science requirements. The trajectory loops through the asteroid belt during 7 years, visiting 10 objects of a wide range of characteristics, providing sufficient survey time to obtain compositional information for 10,000s of objects and the serendipitous discovery of also 10,000s of 10-m class asteroids. The methodology developed has enabled the exploration of the entire design space for a conservative Soyuz-launch performance, and has found a total of 200 different tour opportunities of the asteroid belt; all compliant with ESA’s 5th call for medium size missions

Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc

Optimization of Multiple-Rendezvous Low-Thrust Missions on General-Purpose Graphics Processing Units

A massively parallel method for the identification of optimal sequences of targets in multiple-rendezvous low-thrust missions is presented. Given a list of possible targets, a global search of sequences compatible with the mission requirements is performed. To estimate the feasibility of each transfer, a heuristic model based on Lambert's transfers is evaluated in parallel for each target, making use of commonly available general-purpose graphics processing units such as the Nvidia Tesla cards. The resulting sequences are ranked by user-specified criteria such as length or fuel consumption. The resulting preliminary sequences are then optimized to a full low-thrust trajectory using classical methods for each leg. The performance of the method is discussed as a function of various parameters of the algorithm. The efficiency of the general-purpose graphics processing unit implementation is demonstrated by comparing it with a traditional CPU-based branch-and-bound method. Finally, the algorithm is used to compute asteroid sequences used in a solution submitted to the seventh edition of the Global Trajectory Optimization Competition

Shape and spin distributions of asteroid populations from brightness variation estimates and large databases

Context. Many databases on asteroid brightnesses (e.g. ALCDEF, WISE) are potential sources for extensive asteroid shape and spin modelling. Individual lightcurve inversion models require several apparitions and hundreds of data points per target. However, we can analyse the coarse shape and spin distributions over populations of at least thousands of targets even if there are only a few points and one apparition per asteroid. This is done by examining the distribution of the brightness variations observed within the chosen population. Aims. Brightness variation has been proposed as a population-scale rather than individual-target observable in two studies so far. We aim to examine this approach rigorously to establish its theoretical validity, degree of ill-posedness, and practical applicability. Methods. We model the observed brightness variation of a target population by considering its cumulative distribution function (CDF) caused by the joint distribution function of two fundamental shape and spin indicators. These are the shape elongation and the spin latitude of a simple ellipsoidal model. The main advantage of the model is that we can derive analytical basis functions that yield the observed CDF as a function of the shape and spin distribution. The inverse problem can be treated linearly. Even though the inaccuracy of the model is considerable, databases of thousands of targets should yield some information on the distribution. Results. We establish the theoretical soundness and the typical accuracy limits of the approach both analytically and numerically. Using simulations, we derive a practical estimate of the model distribution in the (shape, spin)-plane. We show that databases such as Wide-field Infrared Survey Explorer (WISE) yield coarse but robust estimates of this distribution, and as an example compare various asteroid families with each other.Comment: 16 pages, 21 figures, manuscript accepted in Astronomy & Astrophysics, to be published in section 10. Planets and planetary system

Asteroid selection for mission opportunities

A study to assess the present state of knowledge of asteroids as well as the rate of change of that knowledge to better identify the mission and target priorities for advanced planning of asteroidal flights in the 1980's is presented. Topics discussed include; the present state of asteroid knowledge, the scientific goals and priorities attached to asteroid exploration, the anticipated advances in knowledge over the current decade, asteroid mission consideration, and asteroid selection. Data sheets for 118 asteroids are contained. These are asteroids for which some data is available over and above orbital parameters and magnitude

Resources of Near-Earth Space: Abstracts

The objectives are by theory, experiment, and bench-level testing of small systems, to develop scientifically-sound engineering processes and facility specifications for producing propellants and fuels, construction and shielding materials, and life support substances from the lithospheres and atmospheres of lunar, planetary, and asteroidal bodies. Current emphasis is on the production of oxygen, other usefull gases, metallic, ceramic/composite, and related byproducts from lunar regolith, carbonaceous chrondritic asteroids, and the carbon dioxide rich Martian atmosphere

A tabu search methodology for spacecraft tour trajectory optimization

textA spacecraft tour trajectory is a trajectory in which a spacecraft visits a number of objects in sequence. The target objects may consist of satellites, moons, planets or any other body in orbit, and the spacecraft may visit these in a variety of ways, for example flying by or rendezvousing with them. The key characteristic is the target object sequence which can be represented as a discrete set of decisions that must be made along the trajectory. When this sequence is free to be chosen, the result is a hybrid discrete-continuous optimization problem that combines the challenges of discrete and combinatorial optimization with continuous optimization. The problem can be viewed as a generalization of the traveling salesman problem; such problems are NP-hard and their computational complexity grows exponentially with the problem size. The focus of this dissertation is the development of a novel methodology for the solution of spacecraft tour trajectory optimization problems. A general model for spacecraft tour trajectories is first developed which defines the parameterization and decision variables for use in the rest of the work. A global search methodology based on the tabu search metaheuristic is then developed. The tabu search approach is extended to operate on a tree-based solution representation and neighborhood structure, which is shown to be especially efficient for problems with expensive solution evaluations. Concepts of tabu search including recency-based tabu memory and strategic intensification and diversification are then applied to ensure a diverse exploration of the search space. The result is an automated, adaptive and efficient search algorithm for spacecraft tour trajectory optimization problems. The algorithm is deterministic, and results in a diverse population of feasible solutions upon termination. A novel numerical search space pruning approach is then developed, based on computing upper bounds to the reachable domain of the spacecraft, to accelerate the search. Finally, the overall methodology is applied to the fourth annual Global Trajectory Optimization Competition (GTOC4), resulting in previously unknown solutions to the problem, including one exceeding the best known in the literature.Aerospace Engineerin

Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks

Asteroid exploration has been attracting more attention in recent years. Nevertheless, we have just visited tens of asteroids while we have discovered more than one million bodies. As our current observation and knowledge should be biased, it is essential to explore multiple asteroids directly to better understand the remains of planetary building materials. One of the mission design solutions is utilizing asteroid flyby cycler trajectories with multiple Earth gravity assists. An asteroid flyby cycler trajectory design problem is a subclass of global trajectory optimization problems with multiple flybys, involving a trajectory optimization problem for a given flyby sequence and a combinatorial optimization problem to decide the sequence of the flybys. As the number of flyby bodies grows, the computation time of this optimization problem expands maliciously. This paper presents a new method to design asteroid flyby cycler trajectories utilizing a surrogate model constructed by deep neural networks approximating trajectory optimization results. Since one of the bottlenecks of machine learning approaches is the computation time to generate massive trajectory databases, we propose an efficient database generation strategy by introducing pseudo-asteroids satisfying the Karush-Kuhn-Tucker conditions. The numerical result applied to JAXA's DESTINY+ mission shows that the proposed method is practically applicable to space mission design and can significantly reduce the computational time for searching asteroid flyby sequences