Earth Observation Satellite Scheduling with Graph Neural Networks

• URL: http://arxiv.org/abs/2408.15041v1
• Date: Tue, 27 Aug 2024 13:10:26 GMT
• Title: Earth Observation Satellite Scheduling with Graph Neural Networks
• Authors: Antoine Jacquet, Guillaume Infantes, Nicolas Meuleau, Emmanuel Benazera, Stéphanie Roussel, Vincent Baudoui, Jonathan Guerra,
• Abstract summary: This paper presents a new technique for selecting and scheduling observations based on Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) Our simulations show that it is able to learn on small problem instances and generalize to larger real-world instances, with very competitive performance compared to traditional approaches.
• Score: 1.1684839631276702
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The Earth Observation Satellite Planning (EOSP) is a difficult optimization problem 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 oversubscribed: there are much more candidate observations than what can possibly be achieved. Therefore, one must select the set of observations that will be performed while maximizing their weighted cumulative benefit, and propose a feasible 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 problem instances and generalize to larger real-world instances, with very competitive performance compared to traditional approaches.

Related papers

• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies [0.0]
  This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet) It links observational nodes (e.g., surveillance cameras) to events within defined geographical regions, enabling efficient monitoring. Using data from Real-Time Crime Camera (RTCC) systems and Calls for Service (CFS) in New Orleans, we address the network's initial observational imbalances.
  arXiv  Detail & Related papers  (2024-04-22T17:46:29Z)
• Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning [0.6976905094072174]
  We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO) We show that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.
  arXiv  Detail & Related papers  (2023-11-29T21:23:30Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Space-Time Graph Neural Networks with Stochastic Graph Perturbations [100.31591011966603]
  Space-time graph neural networks (ST-GNNs) learn efficient graph representations of time-varying data. In this paper we revisit the properties of ST-GNNs and prove that they are stable to graph stabilitys. Our analysis suggests that ST-GNNs are suitable for transfer learning on time-varying graphs.
  arXiv  Detail & Related papers  (2022-10-28T16:59:51Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Multi-strip observation scheduling problem for ac-tive-imaging agile earth observation satellites [0.0]
  We investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite (MOSP) A bi-objective optimization model is presented along with an adaptive bi-objective memetic algorithm which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a nondominated sorting genetic algorithm II (NSGA-II) Our model is more versatile than existing models and provide enhanced capabilities in applied problem solving.
  arXiv  Detail & Related papers  (2022-07-04T08:35:57Z)
• Transfer RL across Observation Feature Spaces via Model-Based Regularization [9.660642248872973]
  In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations. We propose a novel algorithm which extracts the latent-space dynamics in the source task, and transfers the dynamics model to the target task. Our algorithm works for drastic changes of observation space without any inter-task mapping or any prior knowledge of the target task.
  arXiv  Detail & Related papers  (2022-01-01T22:41:19Z)
• Observation Space Matters: Benchmark and Optimization Algorithm [20.503293998529024]
  We propose a search algorithm to find the optimal observation spaces. We demonstrate that our algorithm significantly improves learning speed compared to manually designed observation spaces.
  arXiv  Detail & Related papers  (2020-11-02T05:40:31Z)
• Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification [70.50751397870972]
  FastGAT is a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks.
  arXiv  Detail & Related papers  (2020-06-15T22:07:54Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.