Earth Observation Satellite Scheduling with Graph Neural Networks

Guillaume Infantes, Antoine Jacquet, Emmanuel Benazera, Stéphanie Roussel, Nicolas Meuleau, Vincent Baudoui, Jonathan Guerra

Keywords: Scheduling, Graph neural Networks, Reinforcement learning, Earth Observation

TL;DR: Graph Neural Networks and Deep Reinforcement Learning for Earth Observation Scheduling

Abstract:

The Earth Observation Satellite Planning (EOSP) is a difficult optimization prob-
lem with considerable practical interest. A set of requested observations must
be scheduled on an agile Earth observation satellite while respecting constraints
on their visibility window, as well as maneuver constraints that impose varying
delays between successive observations. In addition, the problem is largely over-
subscribed: there are much more candidate observations than what can possibly
be achieved. Therefore, one must select the set of observations that will be per-
formed while maximizing their weighted cumulative benefit, and propose a feasi-
ble schedule for these observations. As previous work mostly focused on heuristic
and iterative search algorithms, this paper presents a new technique for selecting
and scheduling observations based on Graph Neural Networks (GNNs) and Deep
Reinforcement Learning (DRL). GNNs are used to extract relevant information
from the graphs representing instances of the EOSP, and DRL drives the search
for optimal schedules. Our simulations show that it is able to learn on small prob-
lem instances and generalize to larger real-world instances, with very competitive
performance compared to traditional approaches.

Submission Number: 87