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

Artificial Neural Network Design for Tours of Multiple Asteroids

Designing multiple near-Earth asteroid (NEA) rendezvous missions is a complex global optimization problem, which involves the solution of a large combinatorial part to select the sequences of asteroids to visit. Given that more than 22,000 NEAs are known to date, trillions of permutations between asteroids need to be considered. This work develops a method based on Artificial Neural Networks (ANNs) to quickly estimate the cost and duration of low-thrust transfers between asteroids. The capability of the network to map the relationship between the characteristics of the departure and arrival orbits and the transfer cost and duration is studied. To this end, the optimal network architecture and hyper-parameters are identified for this application. An analysis of the type of orbit parametrization used as network inputs for best performance is performed. The ANN is employed within a sequence-search algorithm based on a tree-search method, which identifies multiple rendezvous sequences and selects those with lowest time of flight and propellant mass needed. To compute the full trajectory and control history, the sequences are subsequently optimized using an optimal control solver based on a pseudospectral method. The performance of the proposed methodology is assessed by investigating NEA sequences of interest. Results show that ANN can estimate the cost or duration of optimal low-thrust transfers with high accuracy, resulting into a mean relative error of less than 4%

Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

In this paper, neural networks are trained to learn the optimal time, the initial costates, and the optimal control law of time-optimal low-thrust interplanetary trajectories. The aim is to overcome the difficult selection of first guess costates in indirect optimization, which limits their implementation in global optimization and prevents on-board applications. After generating a dataset, three networks that predict the optimal time, the initial costate, and the optimal control law are trained. A performance assessment shows that neural networks are able to predict the optimal time and initial costate accurately, especially a 100% success rate is achieved when neural networks are used to initialize the shooting function of indirtect methods. Moreover, learning the state-control pairs shows that neural networks can be utilized in real-time, on-board optimal control

Artificial neural networks for multiple NEA rendezvous missions with continuous thrust

The interest for near-Earth asteroids for scientific studies and, in particular, for potentially hazardous asteroids requires the space community to perform multiple-asteroid missions with close-up observations. To this end, multiple near-Earth asteroid rendezvous missions can help reduce the cost of the mission. Given the enormous number of asteroids, this work proposes a method based on artificial neural networks (ANNs) to quickly estimate the transfer time and cost between asteroids using low-thrust propulsion. The neural network output is used in a sequence search algorithm based on a tree-search method to identify feasible sequences of asteroids to rendezvous. The rendezvous sequences are optimized by solving an optimal control problem for each leg to verify the feasibility of the transfer. The effectiveness of the presented methodology is assessed through sequences of asteroids of interest optimized using two low-thrust propulsion systems, namely solar electric propulsion and solar sailing. The results show that ANNs are able to estimate the duration and cost of low-thrust transfers with high accuracy in a modest computational time

Target evaluation and low-thrust trajectory planning for near-Earth asteroid mining

Near-Earth Asteroids (NEAs) are abundant with minerals that would be undoubtedly beneficial for future space exploration, as the utilization of these in-space resources could enable otherwise unaffordable missions. This thesis aims to address several remaining issues in asteroid mining mission planning, including target selection and ranking, multi-return low thrust trajectory design, NEA mining season determination, asteroid mining campaign designs, and other considerations. This study starts with a comprehensive asteroid resource investigation and an impulsive roundtrip accessibility analysis for the known 29,266 NEAs and 46% of them are found accessible. By combining the two studies, a NEA resource map is created, providing key knowledge of resource locations, types, reserves, and minimum delta-V requirements to retrieve the resources. Mining missions are then preliminarily constructed using impulsive trajectories for 13,481 NEAs, and a series of Figures of Merit (FoMs) are proposed. In total, over 900 accessible and known targets for mining water, Platinum Group Metals (PGMs) and silicates are ranked. Low-thrust mining missions are then studied. New Deep Neural Network (DNN) based models are constructed as the surrogate of the conventional optimization process. The new method reduces by 99.94% the low-thrust trajectory design time. Typical Solar Electric Propulsion (SEP) spacecraft configurations are used to design trajectories for supply delivery and resource transportation. The transportation capabilities of different spacecraft configurations are quantified. An asteroid mining campaign design framework is proposed, which integrates all the developed models, algorithms, and asteroid data. An example mining campaign on Bennu is presented, and an economic analysis is performed. The sensitivity analysis shows the low thrust mining missions are more resistant to changing economic parameters. Campaigns are then numerically designed and optimized for 76 known water-bearing and 58 potential PGM-bearing targets, using both impulsive and low thrust trajectories. The “NEA mining season”, which was an abstract concept, is validated. The mining seasons are categorized into three major types based on their feasibility for mining. Two 35-year water mining and PGM mining plans are generated. It is found the current known targets can form a $21,000 billion PGM mining industry and a$13,000 billion water mining industry. It is found that low thrust-based mining is the key to a successful mining campaign, and that it may increase the profit by 2.8 ~ 8.7 times