Automated Data Accountability for Missions in Mars Rover Data

Alimo, Ryan; Dylan, Sam,; Lakhmiri, Dounia; Kahovec, Brian

doi:10.1109/aero50100.2021.9438220

Cited by 6 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, comparisons of the ∆-MILP results with other methodologies such as Deep Reinforcement Learning and Quantum Computing solutions will be studied in future publications. ∆-MILP will be extended to many other applications in the future such as stabilization of balloons [30], multi-agent motion planning [31], automated data accountability for Mars missions [32] and derivativefree optimization [33].…”

Section: Discussionmentioning

confidence: 99%

Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Anomaly detection for data accountability of Mars telemetry data

Lakhmiri¹,

Alimo²,

Digabel³

2022

Expert Systems with Applications

View full text Add to dashboard Cite

Deep Space Network Scheduling via Mixed-Integer Linear Programming

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Automated Data Accountability for Missions in Mars Rover Data

Cited by 6 publications

References 31 publications

Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

Anomaly detection for data accountability of Mars telemetry data

Deep Space Network Scheduling via Mixed-Integer Linear Programming

Contact Info

Product

Resources

About