HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster
Quick Answer
The HADT model introduces a transformer-based architecture for autonomous resource management in heterogeneous satellite clusters, enhancing real-time decision-making for Earth Observation missions.
Quick Take
The HADT model introduces a transformer-based architecture for autonomous resource management in heterogeneous satellite clusters, enhancing real-time decision-making for Earth Observation missions. It outperforms traditional optimization methods, demonstrating significant adaptability and transferability across varying satellite configurations.
Key Points
- HADT employs model-free reinforcement learning for adaptive resource management.
- The architecture features relational observations-actions tokenization and differential attention.
- Experimental results show significant performance improvements over existing baselines.
- HADT is designed to operate with minimal ground operator interaction.
- The model adapts effectively to varying numbers of satellites in a cluster.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 31023v1 Announce Type: new Abstract: This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions including optical and Synthetic Aperture Radar (SAR) satellites. In autonomous operation mode, satellites are equipped with intelligent capabilities enabling real-time decision-making based on the latest conditions, while requiring minimal interaction with ground operators.
Traditional scheduling approaches typically rely on mathematical models to represent satellite mission and resource management. Then, this problem is solved by using optimization algorithms. However, such solutions become less effective when the underlying models are not available, over complex, and inaccurate due to dynamic changes and uncertainties inherent in the space mission environment.
A promising alternative is to reformulate the problem as a sequential decision-making process and apply model-free reinforcement learning techniques to enable adaptive and real-time resource management. To this end, we propose a novel transformer-based architecture tailored for heterogeneous satellite cluster autonomous EO Mission with relational observations-actions tokenization and differential attention mechanism.
Our experimental results demonstrate significant performance improvements compared to the available baselines. Moreover, the proposed architecture exhibits strong adaptability and transferability with respect to varying numbers of satellite clusters.
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