End to End Satellite Servicing and Space Debris Management

There is growing demand for satellite swarms and constellations for global positioning, remote sensing and relay communication in higher LEO orbits. This will result in many obsolete, damaged and abandoned satellites that will remain on-orbit beyond 25 years. These abandoned satellites and space debris maybe economically valuable orbital real-estate and resources that can be reused, repaired or upgraded for future use. Space traffic management is critical to repair damaged satellites, divert satellites into warehouse orbits and effectively de-orbit satellites and space debris that are beyond repair and salvage. Current methods for on-orbit capture, servicing and repair require a large service satellite. However, by accessing abandoned satellites and space debris, there is an inherent heightened risk of damage to a servicing spacecraft. Sending multiple small-robots with each robot specialized in a specific task is a credible alternative, as the system is simple and cost-effective and where loss of one or more robots does not end the mission. In this work, we outline an end to end multirobot system to capture damaged and abandoned spacecraft for salvaging, repair and for de-orbiting. We analyze the feasibility of sending multiple, decentralized robots that can work cooperatively to perform capture of the target satellite as a first step, followed by crawling onto damage satellites to perform detailed mapping. After obtaining a detailed map of the satellite, the robots will proceed to either repair and replace or dismantle components for salvage operations. Finally, the remaining components will be packaged with a de-orbit device for accelerated de-orbit.Comment: 13 pages, 10 figures, Space Traffic Management Conference. arXiv admin note: text overlap with arXiv:1809.02028, arXiv:1809.04459, arXiv:1901.0971

Six Degree-of-Freedom Body-Fixed Hovering over Unmapped Asteroids via LIDAR Altimetry and Reinforcement Meta-Learning

We optimize a six degrees of freedom hovering policy using reinforcement meta-learning. The policy maps flash LIDAR measurements directly to on/off spacecraft body-frame thrust commands, allowing hovering at a fixed position and attitude in the asteroid body-fixed reference frame. Importantly, the policy does not require position and velocity estimates, and can operate in environments with unknown dynamics, and without an asteroid shape model or navigation aids. Indeed, during optimization the agent is confronted with a new randomly generated asteroid for each episode, insuring that it does not learn an asteroid's shape, texture, or environmental dynamics. This allows the deployed policy to generalize well to novel asteroid characteristics, which we demonstrate in our experiments. Moreover, our experiments show that the optimized policy adapts to actuator failure and sensor noise. Although the policy is optimized using randomly generated synthetic asteroids, it is tested on two shape models from actual asteroids: Bennu and Itokawa. We find that the policy generalizes well to these shape models. The hovering controller has the potential to simplify mission planning by allowing asteroid body-fixed hovering immediately upon the spacecraft's arrival to an asteroid. This in turn simplifies shape model generation and allows resource mapping via remote sensing immediately upon arrival at the target asteroid.Comment: Earlier version presented at 2020 AIAA Scitech conference. arXiv admin note: substantial text overlap with arXiv:1907.06098, arXiv:1906.0211

Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike

We use deep reinforcement learning (RL) to optimize a weapons to target assignment (WTA) policy for multi-vehicle hypersonic strike against multiple targets. The objective is to maximize the total value of destroyed targets in each episode. Each randomly generated episode varies the number and initial conditions of the hypersonic strike weapons (HSW) and targets, the value distribution of the targets, and the probability of a HSW being intercepted. We compare the performance of this WTA policy to that of a benchmark WTA policy derived using non-linear integer programming (NLIP), and find that the RL WTA policy gives near optimal performance with a 1000X speedup in computation time, allowing real time operation that facilitates autonomous decision making in the mission end game

Space Objects Classification and Characterization via Deep Learning and Light Curves: Applications to Space Traffic Management

Recent advancements in deep learning (e.g. Convolutional Neural Networks (CNN), Recurrent Neural networks (RNN)) have demonstrated impressive results in many practical and theoretical fields (e.g. speech recognition, computer vision, robotics). Whereas deep learning methods are becoming ubiquitous, they have been barely explored in SSA applications, in particular with regard to object characterization for Space Traffic Management (STM). In this paper, we report the results obtained in designing and training a set of CNNs and RNNs for Space Object (SO) classification and characterization using light-curve measurements. More specifically, we provide a comparison between deep networks trained on both physically-based models (i.e. reflectance models) and real light-curve data (data-driven approach). Physically-based models allow the generation of synthetic light-curves as function of the SO parameters (e.g. shape, size, material). Such models have the ability to yield a large number of training points which make them suitable to train deep networks. Nevertheless, they suffer from modeling errors. Conversely, real light-curve measurements can be employed to directly train CNNs and/or RNNs, yet with a relatively limited data set. Insight in how to design such networks and expected performances will be discussed to highlight the power of the proposed methodology. Additionally, a cluster analysis conducted via data dimensionality reductions coupled with unsupervised feature extractions for SO characterization will be presented

Development of non-linear guidance algorithms for asteroids close-proximity operations

In this paper, we discuss non-linear methodologies that can be employed to devise real-time algorithms suitable for guidance and control of spacecrafts during asteroid close-proximity operations. Combination of optimal and sliding control theory provide the theoretical framework for the development of guidance laws that generates thrust commands as function of the estimated spacecraft state. Using a Lyapunov second theorem one can design non-linear guidance laws that are proven to be globally stable against unknown perturbations with known upper bound. Such algorithms can be employed for autonomous targeting of points of the asteroid surface (soft landing , Touch-And-Go (TAG) maneuvers). Here, we theoretically derived and tested the Optimal Sliding Guidance (OSG) for close-proximity operations. The guidance algorithm has its root in the generalized ZEM/ZEV feedback guidance and its mathematical equations are naturally derived by a proper definition of a sliding surface as function of Zero-Effort-Miss and Zero-Effort-Velocity. Thus, the sliding surface allows a natural augmentation of the energy-optimalguidance via a sliding mode that ensures global stability for the proposed algorithm. A set of Monte Carlo simulations in realistic environment are executed to assess the guidance performance in typical operational scenarios found during asteroids close-proximity operations. OSG is shown to satisfy stringent requirements for asteroid pinpoint landing and sampling accuracy

“On the Foundation of Transport-Driven Diffusion for Neutron Transport Problems”

The article presents the foundation of a novel methodology developed for the solution of the neutron transport equation, named the transport driven-diffusion approach, which can be considered as an evolution of the classic multiple collision method. The idea behind this method is based on the expansion of the full solution in terms of the contributions of the particles emitted by successive collisions plus a residual term, accounting for particles which have undergone more than a predefined number of collisions. In order to determine the contribution at each collision order, a transport equation with a source term is solved, while the estimation of the residue is based on a diffusion theory model. The physical rationale for the choice of the diffusion model for the residue is discussed and justified, as physics suggests that the diffusion assumptions become more applicable for the description of the particles having suffered a certain number of collisions rather than to the original transport problem. Some results are presented for a set of steady-state and time-dependent test cases. Their analysis shows the remarkable advantage of the method proposed in terms of accuracy and computational time, when compared to standard diffusion and multiple collision at the same order