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Multi-Robot Strategies for Communication-Constrained Exploration and Electrostatic Anomaly Characterization

Gjosse Zijlstra, Karen L. Aplin, Edmund R. Hunt

Abstract

Exploration of extreme or remote environments such as Mars is often recognized as an opportunity for multi-robot systems. However, this poses challenges for maintaining robust inter-robot communication without preexisting infrastructure. It may be that robots can only share information when they are physically in close proximity with each other. At the same time, atmospheric phenomena such as dust devils are poorly understood and characterization of their electrostatic properties is of scientific interest. We perform a comparative analysis of two multi-robot communication strategies: a distributed approach, with pairwise intermittent rendezvous, and a centralized, fixed base station approach. We also introduce and evaluate the effectiveness of an algorithm designed to predict the location and strength of electrostatic anomalies, assuming robot proximity. Using an agent-based simulation, we assess the performance of these strategies in a 2D grid cell representation of a Martian environment. Results indicate that a decentralized rendezvous system consistently outperforms a fixed base station system in terms of exploration speed and in reducing the risk of data loss. We also find that inter-robot data sharing improves performance when trying to predict the location and strength of an electrostatic anomaly. These findings indicate the importance of appropriate communication strategies for efficient multi-robot science missions.

Multi-Robot Strategies for Communication-Constrained Exploration and Electrostatic Anomaly Characterization

Abstract

Exploration of extreme or remote environments such as Mars is often recognized as an opportunity for multi-robot systems. However, this poses challenges for maintaining robust inter-robot communication without preexisting infrastructure. It may be that robots can only share information when they are physically in close proximity with each other. At the same time, atmospheric phenomena such as dust devils are poorly understood and characterization of their electrostatic properties is of scientific interest. We perform a comparative analysis of two multi-robot communication strategies: a distributed approach, with pairwise intermittent rendezvous, and a centralized, fixed base station approach. We also introduce and evaluate the effectiveness of an algorithm designed to predict the location and strength of electrostatic anomalies, assuming robot proximity. Using an agent-based simulation, we assess the performance of these strategies in a 2D grid cell representation of a Martian environment. Results indicate that a decentralized rendezvous system consistently outperforms a fixed base station system in terms of exploration speed and in reducing the risk of data loss. We also find that inter-robot data sharing improves performance when trying to predict the location and strength of an electrostatic anomaly. These findings indicate the importance of appropriate communication strategies for efficient multi-robot science missions.
Paper Structure (17 sections, 9 figures, 1 table, 5 algorithms)

This paper contains 17 sections, 9 figures, 1 table, 5 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Methods
  4. Communication-Constrained Exploration
  5. Environment Description
  6. Fixed Base Station Communication System
  7. Intermittent Rendezvous Communication System
  8. Electrostatic Anomaly Characterization
  9. Environment Description
  10. Dust Devil Locating Algorithm
  11. Results
  12. Communication-Constrained Exploration
  13. Electrostatic Anomaly Characterization
  14. Discussion and Conclusion
  15. Communication-Constrained Exploration
  16. ...and 2 more sections

Figures (9)

  • Figure 1: JAXA moon robots, LEV-1 and LEV-2.
  • Figure 2: Rover robot with electric field mill for electrostatic electricity mapping zijlstra2024.
  • Figure 3: Simulated Martian terrain map with rugged and impassable features
  • Figure 4: An example of initial conditions for an electrostatic anomaly experiment. The colour of the grid represents the electrostatic environment, and red crosses the robots.
  • Figure 5: Flowchart of the '$\mathrm{extended\_gradients}$' algorithm.
  • ...and 4 more figures