Consensus Based Task Allocation for Angles-Only Local Catalog Maintenance of Satellite Systems

2026-02-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Engineering & Applied Sciences · Depth: Advanced, extended

Summary

A novel decentralized task allocation algorithm has been developed for multi-satellite systems to maintain local catalogs of communicating and non-communicating objects using angles-only, limited field-of-view measurements. This algorithm, which improves upon previous work by Hays et al., addresses the challenge of efficiently scheduling and coordinating observations among agents. It utilizes a modified Consensus-Based Bundle Algorithm (CBBA) with a new observation scoring function that considers angular distance, target uncertainty, and observation direction, along with a dynamic target switching logic. Numerical simulations involving two agents and eight objects demonstrated that this new method significantly outperforms the uncertainty-fuel Pareto frontier established by current hysteresis-based approaches, achieving better overall performance in terms of fuel usage and maintaining object uncertainty below a predefined threshold. The algorithm's parameters, such as planning depth and discount factor, were evaluated, showing optimal performance at a discount factor of 0.1 and an alpha value of 0.1.

Key takeaway

For AI Scientists designing multi-agent satellite systems for space situational awareness, adopting this new decentralized task allocation algorithm can significantly reduce fuel consumption while improving the accuracy of object catalog maintenance. You should integrate the modified CBBA with the proposed observation scoring function and dynamic switching logic to achieve superior performance compared to traditional hysteresis-based methods. Consider tuning the discount factor and alpha parameter based on your specific mission parameters, such as the number of agents and objects, to optimize the trade-off between fuel expenditure and uncertainty reduction.

Key insights

A new decentralized algorithm improves satellite catalog maintenance by optimizing observation scheduling and fuel efficiency.

Principles

• Decentralized task allocation enhances robustness.
• Observation quality depends on range, uncertainty, and direction.
• Frequent re-planning is crucial for dynamic environments.

Method

The algorithm uses a modified CBBA with a novel scoring function that weights observation targets by angular distance, uncertainty, and observation direction, coupled with dynamic target switching logic based on the score's rate of decrease.

In practice

• Implement CBBA with a discount factor of 0.1 for fuel efficiency.
• Prioritize targets with high uncertainty and favorable observation angles.
• Consider asynchronous communication for real-world systems.

Topics

• Decentralized Task Allocation
• CBBA
• Satellite Systems
• Angles-Only Sensors
• State Estimation

Best for: AI Scientist, AI Engineer, Robotics Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.