An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship.

Area of Science:

• Earth Science and Remote Sensing
• Aerospace Engineering
• Operations Research

Background:

• High-altitude airships (HAA) are vital for Earth observation in fields like disaster rescue and environmental monitoring.
• Efficient observation scheduling is critical for maximizing HAA capabilities.
• Existing scheduling methods may not fully address the complexities of HAA observation tasks.

Purpose of the Study:

• To develop a hierarchical observation scheduling approach for high-altitude airships (HAA).
• To address the challenges in task clustering, sensor scheduling, and cruise path planning for HAA observation.
• To enhance the efficiency and quality of Earth observation missions conducted by HAAs.

Main Methods:

• A hierarchical observation scheduling approach based on task clustering (SA-TC) is proposed.
• The problem is decomposed into three sub-problems: task clustering, sensor scheduling, and cruise path planning.
• Novel algorithms are developed: a heuristic algorithm with improved ant colony optimization and backtracking for task clustering; a heuristic algorithm with 2-opt local search for sensor scheduling; and improved ant colony optimization for cruise path planning.

Main Results:

• The proposed SA-TC approach demonstrates superiority through extensive simulation experiments.
• The performance of the individual algorithms for the sub-problems is analyzed across different instance scales.
• The integrated approach effectively optimizes HAA observation scheduling.

Conclusions:

• The SA-TC approach provides an effective solution for high-altitude airship observation scheduling.
• The developed algorithms show strong performance in addressing the complex sub-problems.
• This research contributes to more efficient and high-quality Earth observation using HAAs.