Event-Triggered Deep Reinforcement Learning for Dynamic Task Scheduling in Multisatellite Resource Allocation

Cui, Kaixin; Song, Jiliang; Zhang, Lei; Tao, Ying; Liu, Wei; Shi, Dawei

doi:10.1109/taes.2022.3231239

Cited by 14 publications

(6 citation statements)

References 47 publications

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“…R. Lent 18 makes routing decisions based on spiking neural networks (SNN), whose weights are continuously updated with RL, and Liu et al 159 additionally explore an alternative DRL strategy to solve and optimize random access problems. Cui et al 160 incorporate a robust event-triggering strategy with their DRL based dynamic routing structure, enabling efficiency in task scheduling.…”

Section: Advancements In Smart Communications For Satellitesmentioning

confidence: 99%

Networked control systems and their applications to smart satellites: a survey

McCafferty-Leroux,

Wu,

Gadsden

2024

Sensors and Systems for Space Applications XVII

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The advancement of Earth observation satellite research in past decades has demonstrated itself to be productive and increasingly important. Utilized for applications such as climate monitoring, communication, GPS, defense, and space research, our dependence on reliable satellite systems is ever-increasing. The success of satellites in these scenarios is fundamentally the result of its attitude determination system, consisting of control and estimation subsystems, which govern its sensors and actuators. For simple missions, attitude pose determination can be computed onboard the satellite. Typically, however, ground stations or other satellites (i.e. constellations) are involved in a satellite's operation, processing large amounts of data or complex control algorithms. This information and control cycle is enabled through the application of Networked Control Systems (NCS). The NCS uses a wireless network or communication system as the intermediate line of communication between plant, actuators, sensors, and other systems. This enables relatively fast communication and data transmittance over long distances, as well as the decentralization of navigation and control through system distribution. However, this method is vulnerable to various forms of time delay and packet loss, which ultimately affects the control performance of a satellite. It is demonstrated in literature that the effects of these NCS properties can be mitigated, increasing its viability, through various implementations of smart systems into the satellite framework. Using techniques such as neural networks and reinforcement learning, the satellite can perceive and act based on environmental information, while considering experiential memory and attention allocation. The following comprehensive survey discusses methods for improving the robustness of networked satellite systems from a smart systems perspective, providing an advanced foundation for these concepts.

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Section: Advancements In Smart Communications For Satellitesmentioning

confidence: 99%

Networked control systems and their applications to smart satellites: a survey

McCafferty-Leroux,

Wu,

Gadsden

2024

Sensors and Systems for Space Applications XVII

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“…To verify the effectiveness of the MSDMPUP model based on task rolling window (MSDMPUP-TRW), the MSDMPUP-TRW model in this study was compared with the multi-satellite imaging mission planning model based on event triggering (MSIMP-ET) [35], and the multi-satellite imaging mission planning model based on periodic-triggered rolling, MSIMP-PTR [36]. Task sets carrying the target task importance degree were generated using three types of methods.…”

Section: Experiments 1: Effectiveness Of the Msdmpup Model Based On A...mentioning

confidence: 99%

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Yang,

Hu,

Huang

et al. 2024

Electronics

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The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller’s preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP.

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“…The MSIMPUE model based on uncertainty assessment (MSIMPUE-UA) and the Fixed priority setting method (FPSM) are used to participate in the solution of the MSIMPUE. We refer to the literature [31] for a fixed priority setting approach assuming an uncertain environment with a task priority order of: Major mission requirements (Priority Among them, satellite load failure, satellite power supply failure, and internal circuit anomalies necessitate the rapid deployment of satellites to replace problematic satellites for observation, and hence have a high priority.…”

Section: A Experiments Settingsmentioning

confidence: 99%

A Hybrid Local Replanning Strategy for Multi-Satellite Imaging Mission Planning in Uncertain Environments

Yang,

Hu,

Huang

et al. 2023

IEEE Access

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Multi-satellite imaging mission planning (MSIMP) research has advanced substantially in recent years. However, contemporary MSIMP research in uncertain environments is still confronting challenges such as loss of satellite resource allocation, inadequate anti-jamming ability of the mission planning scheme, and low mission completion rate. Therefore, in this work, we propose a hybrid local replanning strategy improved adaptive differential evolutionary (HLRS-MSFADE) algorithm based on the multi-satellite imaging mission planning in uncertain environments (MSIMPUE). First, an MSIMPUE model based on uncertainty assessment is constructed. This model solves the problem of assessing new tasks with varied qualities to decide the observation order in an uncertain environment and decreases the loss caused by inefficient satellite resource allocation. Second, to address the issue of difficulty in planning for changing new task requirements in uncertain environments, an HLRS for uncertain environments is developed to ensure efficient task insertion while avoiding conflict costs. Finally, an MSFADE algorithm is presented to handle the problem of long MSIMPUE mission response time and low mission completion rate with good quality in an acceptable computation time. The simulation results validated the effectiveness and stability of the method in dealing with MSIMPUE. Moreover, the HLRS-MSFADE algorithm outperforms previous methods in terms of mission response time, mission completion rate, and motion perturbation.INDEX TERMS Uncertain environment, multi-satellite imaging mission planning, replanning strategy, adaptive differential evolutionary algorithm.

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Event-Triggered Deep Reinforcement Learning for Dynamic Task Scheduling in Multisatellite Resource Allocation

Cited by 14 publications

References 47 publications

Networked control systems and their applications to smart satellites: a survey

Networked control systems and their applications to smart satellites: a survey

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

A Hybrid Local Replanning Strategy for Multi-Satellite Imaging Mission Planning in Uncertain Environments

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