Autonomous Task Planning for Space-Based Multi-Satellite Interception Using an Improved Genetic Algorithm

Huinan Liu; Hutao Cui; Peng Guo

doi:10.1016/j.ifacol.2025.11.435

Autonomous Task Planning for Space-Based Multi-Satellite Interception Using an Improved Genetic Algorithm

Huinan Liu^*
, Hutao Cui^*
, Peng Guo^*

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

To deal with the task planning problem for intercepting multi-targets with multi-satellites, we develop a decision optimization model, considering complex constraints such as maneuver energy consumption, task duration, task execution sequence, and execution window conflicts. And we propose an improved genetic algorithm based on a [0, 1] interval real-number encoding and decoding scheme for solving the optimization problem. The algorithm can provide sufficient feasible solutions for swarm interception task planning and ensuring optimization efficiency while generating conflict-free task execution sequences and time windows. Simulation results for typical scenarios are presented to validate the effectiveness and feasibility of the proposed algorithm.

Original language	English
Pages (from-to)	1901-1905
Number of pages	5
Journal	IFAC-PapersOnLine
Volume	59
Issue number	20
DOIs	https://doi.org/10.1016/j.ifacol.2025.11.435
State	Published - 1 Aug 2025
Event	23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025 - Harbin, China Duration: 2 Aug 2025 → 6 Aug 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Genetic algorithm
Multi-target interception
Satellite swarm
Task planning

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10.1016/j.ifacol.2025.11.435

Cite this

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title = "Autonomous Task Planning for Space-Based Multi-Satellite Interception Using an Improved Genetic Algorithm",

abstract = "To deal with the task planning problem for intercepting multi-targets with multi-satellites, we develop a decision optimization model, considering complex constraints such as maneuver energy consumption, task duration, task execution sequence, and execution window conflicts. And we propose an improved genetic algorithm based on a [0, 1] interval real-number encoding and decoding scheme for solving the optimization problem. The algorithm can provide sufficient feasible solutions for swarm interception task planning and ensuring optimization efficiency while generating conflict-free task execution sequences and time windows. Simulation results for typical scenarios are presented to validate the effectiveness and feasibility of the proposed algorithm.",

keywords = "Genetic algorithm, Multi-target interception, Satellite swarm, Task planning",

author = "Huinan Liu and Hutao Cui and Peng Guo",

note = "Publisher Copyright: Copyright {\textcopyright} 2025 The Authors.; 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025 ; Conference date: 02-08-2025 Through 06-08-2025",

year = "2025",

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doi = "10.1016/j.ifacol.2025.11.435",

language = "英语",

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TY - JOUR

T1 - Autonomous Task Planning for Space-Based Multi-Satellite Interception Using an Improved Genetic Algorithm

AU - Liu, Huinan

AU - Cui, Hutao

AU - Guo, Peng

PY - 2025/8/1

Y1 - 2025/8/1

N2 - To deal with the task planning problem for intercepting multi-targets with multi-satellites, we develop a decision optimization model, considering complex constraints such as maneuver energy consumption, task duration, task execution sequence, and execution window conflicts. And we propose an improved genetic algorithm based on a [0, 1] interval real-number encoding and decoding scheme for solving the optimization problem. The algorithm can provide sufficient feasible solutions for swarm interception task planning and ensuring optimization efficiency while generating conflict-free task execution sequences and time windows. Simulation results for typical scenarios are presented to validate the effectiveness and feasibility of the proposed algorithm.

AB - To deal with the task planning problem for intercepting multi-targets with multi-satellites, we develop a decision optimization model, considering complex constraints such as maneuver energy consumption, task duration, task execution sequence, and execution window conflicts. And we propose an improved genetic algorithm based on a [0, 1] interval real-number encoding and decoding scheme for solving the optimization problem. The algorithm can provide sufficient feasible solutions for swarm interception task planning and ensuring optimization efficiency while generating conflict-free task execution sequences and time windows. Simulation results for typical scenarios are presented to validate the effectiveness and feasibility of the proposed algorithm.

KW - Genetic algorithm

KW - Multi-target interception

KW - Satellite swarm

KW - Task planning

UR - https://www.scopus.com/pages/publications/105025902839

U2 - 10.1016/j.ifacol.2025.11.435

DO - 10.1016/j.ifacol.2025.11.435

M3 - 会议文章

AN - SCOPUS:105025902839

SN - 2405-8971

VL - 59

SP - 1901

EP - 1905

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 20

T2 - 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025

Y2 - 2 August 2025 through 6 August 2025

ER -

Autonomous Task Planning for Space-Based Multi-Satellite Interception Using an Improved Genetic Algorithm

Abstract

UN SDGs

Keywords

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