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

Harrison Perone, Christopher W. Hays

TL;DR

A decentralized task allocation algorithm is presented that significantly outperforms the uncertainty-fuel Pareto frontier formed by current approaches and quantifies its performance in terms of fuel usage and overall catalog uncertainty via numerical simulation.

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.

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

TL;DR

A decentralized task allocation algorithm is presented that significantly outperforms the uncertainty-fuel Pareto frontier formed by current approaches and quantifies its performance in terms of fuel usage and overall catalog uncertainty via numerical simulation.

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.
Paper Structure (13 sections, 19 equations, 4 figures, 1 algorithm)

This paper contains 13 sections, 19 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Notation and Definitions
  4. Relative Orbital Dynamics
  5. Natural Motion Trajectories
  6. Attitude Dynamics
  7. Principal Axes
  8. Consensus-Based Bundle Algorithm (CBBA)
  9. Approach
  10. Results and Discussion
  11. Evaluation
  12. Simulation Results
  13. Conclusions

Figures (4)

  • Figure 1: Scatter plot of mean performance for CBBA algorithm with different planning depths over a range of random initial conditions. For each configuration, 100 simulations were run, and the 95% confidence interval is shown for each point with error bars.
  • Figure 2: Scatter plot of mean performance for several CBBA discount values over a range of random initial conditions. For each configuration, 100 simulations were run, and the 95% confidence interval is shown for each point with error bars.
  • Figure 3: Scatter plot of mean performance for several CBBA $\alpha$ values over a range of random initial conditions. For each configuration, 100 simulations were run, and the 95% confidence interval is shown for each point with error bars.
  • Figure 4: Scatter plot of mean performance for different algorithm configurations over a range of random initial conditions. For each configuration, 100 simulations were run, and the 95% confidence interval is shown for each point with error bars.