Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions

• URL: http://arxiv.org/abs/2106.03548v1
• Date: Fri, 4 Jun 2021 09:34:20 GMT
• Title: Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions
• Authors: Gauthier Picard
• Abstract summary: We investigate the use of multi-agent allocation techniques on problems related to Earth observation scenarios with multiple users and satellites. As to solve EOSCSP, we propose market-based techniques and a distributed problem solving technique based on Distributed Constraint Optimization. These contributions are experimentally evaluated on randomly generated EOSCSP instances based on real large-scale or highly conflicting observation order books.
• Score: 0.45687771576879593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the use of multi-agent allocation techniques on problems related to Earth observation scenarios with multiple users and satellites. We focus on the problem of coordinating users having reserved exclusive orbit portions and one central planner having several requests that may use some intervals of these exclusives. We define this problem as Earth Observation Satellite Constellation Scheduling Problem (EOSCSP) and map it to a Mixed Integer Linear Program. As to solve EOSCSP, we propose market-based techniques and a distributed problem solving technique based on Distributed Constraint Optimization (DCOP), where agents cooperate to allocate requests without sharing their own schedules. These contributions are experimentally evaluated on randomly generated EOSCSP instances based on real large-scale or highly conflicting observation order books.

Related papers

• On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making [0.0]
  This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs) We propose an autonomous servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM.
  arXiv  Detail & Related papers  (2024-09-25T17:40:37Z)
• Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications [10.652828373995519]
  We critically assess the impact of satellite failures on constellation performance and the associated task requirements. We introduce reinforcement learning (RL) techniques, specifically Q-learning, Policy Gradient, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO) Our results demonstrate that DQN and PPO achieve effective outcomes in terms of average rewards, task completion rates, and response times.
  arXiv  Detail & Related papers  (2024-09-03T20:01:56Z)
• 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)
• Heterogeneity: An Open Challenge for Federated On-board Machine Learning [2.519319150166215]
  We present a systematic review of the challenges in the context of the cross-provider use case for Federated Learning. Such an application presents additional challenges to the Federated Learning paradigm, arising largely from the heterogeneity of such a system.
  arXiv  Detail & Related papers  (2024-08-13T13:56:17Z)
• Scalable Scheduling Policies for Quantum Satellite Networks [10.91414940065524]
  We consider the problem of transmission scheduling in quantum satellite networks subject to resource constraints at the satellites and ground stations. We show that the most general problem of assigning satellites to ground station pairs for entanglement distribution is NP-hard. We propose four scalable algorithms and evaluate their performance for Starlink mega constellation.
  arXiv  Detail & Related papers  (2024-05-15T15:58:12Z)
• Innovations in the field of on-board scheduling technologies [64.41511459132334]
  This paper proposes an onboard scheduler, that integrates inside an onboard software framework for mission autonomy. The scheduler is based on linear integer programming and relies on the use of a branch-and-cut solver. The technology has been tested on an Earth Observation scenario, comparing its performance against the state-of-the-art scheduling technology.
  arXiv  Detail & Related papers  (2022-05-04T12:00:49Z)
• Learning Emergent Random Access Protocol for LEO Satellite Networks [51.575090080749554]
  We propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH) eRACH is a model-free approach that emerges through interaction with the non-stationary network environment. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput.
  arXiv  Detail & Related papers  (2021-12-03T07:44:45Z)
• A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations [41.013477422930755]
  This paper introduces a new approach for solving the satellite scheduling problem by generating an infeasibility-based graph representation of the problem. It is tested on a scenarios of up to 10,000 requested imaging locations for the Skysat constellation of optical satellites as well as simulated constellations of up to 24 satellites.
  arXiv  Detail & Related papers  (2020-08-15T19:32:21Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)
• 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.