Cislunar Satellite Constellation Design Via Integer Linear Programming

Cislunar space awareness is of increasing interest to the international community as Earth-Moon traffic is projected to increase. This raises the problem of placing satellites optimally in a constellation to provide satisfactory coverage for said traffic. The Circular Restricted 3 Body Problem (CR3BP) provides promising periodic orbits in the Earth-Moon rotating frame for traffic monitoring. This work converts a spatially and temporally varying traffic coverage requirement into an integer linear programming problem, attempting to minimize the number of satellites required for the requested coverage.Comment: 18 pages, 15 figures, submitted to 2023 AAS Conferenc

ORACLE : A sample-return mission to Titan

With a hazy atmosphere, a hydrocarbon cycle, seasons, and a diverse set of surface features, Titan is one of the most unique objects in the Solar System. Further exploration of Titan can elucidate its geologic activity, chemical history, and astrobiological potential. While one-way missions can provide a wealth of information about Titan through remote sensing, in-situ measurements, and communication relays back to Earth, returning samples from Titan allows for unparalleled scientific analysis. Here, we propose a novel mission concept to explore and analyze Titan in situ and return samples from its hydrocarbon lakes. Within ORACLE, a separate lander and orbiter segment will perform all the scientific investigations and collect the hydrocarbon lake samples. After collection of the samples, another segment will return the samples to Earth while the lander and orbiter continue investigating Titan. This mission concept demonstrates novel Titan lake sampling technology and incorporates sample return and in-situ scientific investigation to significantly increase our understanding of Titan, with far broader planetary science implications

A Search for Technosignatures Around 31 Sun-like Stars with the Green Bank Telescope at 1.15-1.73 GHz

We conducted a search for technosignatures in April of 2018 and 2019 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused on regions surrounding 31 Sun-like stars near the plane of the Galaxy. We present the results of our search for narrowband signals in this data set as well as improvements to our data processing pipeline. Specifically, we applied an improved candidate signal detection procedure that relies on the topographic prominence of the signal power, which nearly doubles the signal detection count of some previously analyzed data sets. We also improved the direction-of-origin filters that remove most radio frequency interference (RFI) to ensure that they uniquely link signals observed in separate scans. We performed a preliminary signal injection and recovery analysis to test the performance of our pipeline. We found that our pipeline recovers 93% of the injected signals over the usable frequency range of the receiver and 98% if we exclude regions with dense RFI. In this analysis, 99.73% of the recovered signals were correctly classified as technosignature candidates. Our improved data processing pipeline classified over 99.84% of the ~26 million signals detected in our data as RFI. Of the remaining candidates, 4539 were detected outside of known RFI frequency regions. The remaining candidates were visually inspected and verified to be of anthropogenic nature. Our search compares favorably to other recent searches in terms of end-to-end sensitivity, frequency drift rate coverage, and signal detection count per unit bandwidth per unit integration time.Comment: 20 pages, 8 figures, in press at the Astronomical Journal (submitted on Sept. 9, 2020; reviews received Nov. 6; re-submitted Nov. 6; accepted Nov. 17

On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

© AIAAThis paper proposes an on-orbit servicing logistics optimization framework capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-the-art on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. Mixed-integer linear programming is leveraged to find the optimal operations of the servicers over a given period, whereas the rolling horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high- and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.This work is supported by the Defense Advanced Research Project Agency Young Faculty Award D19AP00127

The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11

Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output. During the 11th edition of the GTOC challenge, participants were tasked with a complex trajectory planning related to the construction of a precursor Dyson structure, a heliocentric ring made of twelve stations. To this purpose, we developed several new approaches that synthesize techniques from machine learning, combinatorial optimization, planning and scheduling, and evolutionary optimization effectively integrated into a fully automated pipeline. These include a machine learned transfer time estimator, improving the established Edelbaum approximation and thus better informing a Lazy Race Tree Search to identify and collect asteroids with high arrival mass for the stations; a series of optimally-phased low-thrust transfers to all stations computed by indirect optimization techniques, exploiting the synodic periodicity of the system; and a modified Hungarian scheduling algorithm, which utilizes evolutionary techniques to arrange a mass-balanced arrival schedule out of all transfer possibilities. We describe the steps of our pipeline in detail with a special focus on how our approaches mutually benefit from each other. Lastly, we outline and analyze the final solution of our team, ACT&Friends, which ranked second at the GTOC 11 challenge