Consensus Based Task Allocation for Angles-Only Local Catalog Maintenance of Satellite Systems
2026-02-18 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors studied how satellites that fly close to each other can keep track of nearby satellites and space debris to avoid collisions. They focus on a situation where these satellites communicate and use sensors that only measure angles within a limited view to identify other objects. To manage this, the authors developed a method that lets each satellite decide which objects to observe without a central controller, saving fuel and improving tracking accuracy. Through simulations, they showed their method works better than existing approaches in balancing fuel use and tracking uncertainty.
relative state estimationspace-based sensorsangles-only measurementslimited field of viewdecentralized task allocationsatellite catalogingfuel efficiencyorbit determinationmulti-agent coordinationuncertainty management
Authors
Harrison Perone, Christopher W. Hays
Abstract
In order for close proximity satellites to safely perform their missions, the relative states of all satellites and pieces of debris must be well understood. This presents a problem for ground based tracking and orbit determination since it may not be practical to achieve the required accuracy. Using space-based sensors allows for more accurate relative state estimates, especially if multiple satellites are allowed to communicate. Of interest to this work is the case where several communicating satellites each need to maintain a local catalog of communicating and non-communicating objects using angles-only limited field of view (FOV) measurements. However, this introduces the problem of efficiently scheduling and coordinating observations among the agents. This paper presents a decentralized task allocation algorithm to address this problem and quantifies its performance in terms of fuel usage and overall catalog uncertainty via numerical simulation. It was found that the new method significantly outperforms the uncertainty-fuel Pareto frontier formed by current approaches.