NASA's Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its' services makes the assignment of DSN resources a daunting computational problem. In this paper we study the DSN scheduling problem, which is the problem of assigning the DSN's limited resources to its users within a given time horizon. The DSN scheduling problem is oversubscribed, meaning that only a subset of the activities can be scheduled, and network operators must decide which activities to exclude from the schedule. We first formulate this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization problem. Next, we develop a sequential algorithm which solves the resulting MILP formulation to produce valid schedules for large-scale instances of the DSN scheduling problem. We use real world DSN data from week 44 of 2016 in order to evaluate our algorithm's performance. We find that given a fixed run time, our algorithm outperforms a simple implementation of our MILP model, generating a feasible schedule in which 17% more activities are scheduled by the algorithm than by the simple implementation. We design a non-MILP based heuristic to further validate our results. We find that our algorithm also outperforms this heuristic, scheduling 8% more activities and 20% more tracking time than the best results achieved by the non-MILP implementation.
In this paper we describe a machine learning based framework for spacecraft swarm trajectory planning. In particular, we focus on coordinating motions of multi-spacecraft in formation flying through passive relative orbit(PRO) transfers. Accounting for spacecraft dynamics while avoiding collisions between the agents makes spacecraft swarm trajectory planning difficult. Centralized approaches can be used to solve this problem, but are computationally demanding and scale poorly with the number of agents in the swarm. As a result, centralized algorithms are ill-suited for real time trajectory planning on board small spacecraft (e.g. CubeSats) comprising the swarm. In our approach a neural network is used to approximate solutions of a centralized method. The necessary training data is generated using a centralized convex optimization framework through which several instances of the n=10 spacecraft swarm trajectory planning problem are solved. We are interested in answering the following questions which will give insight on the potential utility of deep learning-based approaches to the multispacecraft motion planning problem: 1) Can neural networks produce feasible trajectories that satisfy safety constraints (e.g. collision avoidance) and low in fuel cost? 2) Can a neural network trained using n spacecraft data be used to solve problems for spacecraft swarms of differing size?
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