Simulated annealing based heuristic for multiple agile satellites
scheduling under cloud coverage uncertainty
- URL: http://arxiv.org/abs/2003.08363v2
- Date: Wed, 7 Jul 2021 07:34:40 GMT
- Title: Simulated annealing based heuristic for multiple agile satellites
scheduling under cloud coverage uncertainty
- Authors: Chao Han, Yi Gu, Guohua Wu, Xinwei Wang
- Abstract summary: Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability.
We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty.
An improved simulated annealing based combining a fast insertion strategy is proposed for large-scale observation missions.
- Score: 1.100580615194563
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Agile satellites are the new generation of Earth observation satellites
(EOSs) with stronger attitude maneuvering capability. Since optical remote
sensing instruments equipped on satellites cannot see through the cloud, the
cloud coverage has a significant influence on the satellite observation
missions. We are the first to address multiple agile EOSs scheduling problem
under cloud coverage uncertainty where the objective aims to maximize the
entire observation profit. The chance constraint programming model is adopted
to describe the uncertainty initially, and the observation profit under cloud
coverage uncertainty is then calculated via sample approximation method.
Subsequently, an improved simulated annealing based heuristic combining a fast
insertion strategy is proposed for large-scale observation missions. The
experimental results show that the improved simulated annealing heuristic
outperforms other algorithms for the multiple AEOSs scheduling problem under
cloud coverage uncertainty, which verifies the efficiency and effectiveness of
the proposed algorithm.
Related papers
- A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system.
We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks.
The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem.
arXiv Detail & Related papers (2024-08-29T06:57:45Z) - Earth Observation Satellite Scheduling with Graph Neural Networks [1.1684839631276702]
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.
arXiv Detail & Related papers (2024-08-27T13:10:26Z) - Monte Carlo Tree Search Satellite Scheduling Under Cloud Cover Uncertainty [0.0]
This paper addresses the multi-satellite collection scheduling problem (m-SatCSP)
It aims to optimize task scheduling over a constellation of satellites under uncertain conditions such as cloud cover.
arXiv Detail & Related papers (2024-05-31T15:50:46Z) - SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
Weather forecasting plays a critical role in various sectors, driving decision-making and risk management.
Traditional methods often struggle to capture the complex dynamics of meteorological systems.
We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
arXiv Detail & Related papers (2024-05-29T08:00:15Z) - Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations [53.797896854533384]
Class-agnostic motion prediction methods directly predict the motion of the entire point cloud.
While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming.
We introduce three simple spatial and temporal regularization losses, which facilitate the self-supervised training process effectively.
arXiv Detail & Related papers (2024-03-20T02:58:45Z) - Secure and Efficient Federated Learning in LEO Constellations using
Decentralized Key Generation and On-Orbit Model Aggregation [1.4952056744888915]
This paper proposes FedSecure, a secure FL approach designed for LEO constellations.
FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites.
It also reduces convergence delay drastically from days to only a few hours, yet achieving high accuracy of up to 85.35%.
arXiv Detail & Related papers (2023-09-04T21:36:46Z) - 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) - Three multi-objective memtic algorithms for observation scheduling
problem of active-imaging AEOS [0.0]
We call the novel problem as observation scheduling problem for AEOS with variable image duration (OSWVID)
A cumulative image quality and a detailed energy consumption is proposed to build OSWVID as a bi-objective optimization model.
Three multi-objective memetic algorithms, PD+NSGA-II, LANSGA-II and ALNS+NSGA-II, are then designed to solve OSWVID.
arXiv Detail & Related papers (2022-07-04T08:18:54Z) - Bootstrap Motion Forecasting With Self-Consistent Constraints [52.88100002373369]
We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints.
The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past.
We show that our proposed scheme consistently improves the prediction performance of several existing methods.
arXiv Detail & Related papers (2022-04-12T14:59:48Z) - Reinforcement Learning for Low-Thrust Trajectory Design of
Interplanetary Missions [77.34726150561087]
This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances.
An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted.
The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
arXiv Detail & Related papers (2020-08-19T15:22:15Z) - Agile Earth observation satellite scheduling over 20 years:
formulations, methods and future directions [69.47531199609593]
Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs)
The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs)
arXiv Detail & Related papers (2020-03-13T09:38:40Z)
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.