Future space exploration missions will heavily rely on autonomous planning and execution (APE) technology to improve spacecraft reliability and reduce operational costs. However, this will require a complete revamp of ground operations, i.e., from current practice of specifying pre-planned sequences to specifying high-level goals that will later be elaborated by the onboard APE based on spacecraft’s state and perceived environment. Particularly, determining the mission outcomes during downlink is a challenging task. In this paper, we reconstruct what the spacecraft has executed onboard (i.e., as-executed) using downlinked channelized data, EVRs, and, critically, spacecraft models; We also quantitatively compare as-executed from the "actual" run with ground-based prediction simulations. To do this quantitative comparison, we design an N-dimensional dynamic time warping (DTW) technique based on which we formulate two similarity scores: (a) one related to executed tasks whose cost function is based on interval-based generalized intersection over union; and (b) other related to spacecraft states whose cost function is based on normalized Manhattan distance. Through a simulated case study of multiple flybys in Neptune-Triton system, we demonstrate that our technique successfully quantifies the similarity between the as-executed actual and predicts, and assess its "in-family" versus "out-of-family" behavior. To lower the associated false positives/negatives, we also design a multi-objective assessment metric that is a weighted summation of the task- and timeline-related similarity scores.
Operations for Autonomous Spacecraft: Downlink Analysis of Onboard Decisions and Execution Anomalies
2024-03-02
4498004 byte
Conference paper
Electronic Resource
English