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Efficient Frontier Management for Collaborative Active SLAM

Muhammad Farhan Ahmed, Matteo Maragliano, Vincent FremontCarmine, Tommaso Recchiuto, Antonio Sgorbissa

TL;DR

This work addresses the challenge of coordinating multiple robots in Active Collaborative SLAM by introducing a centralized frontier-sharing framework that optimizes exploration using merged-map information gain, distance, and reward metrics. The methodology combines frontier management to prune candidates, a spread policy to promote even exploration, and synchronous/asynchronous coordination modes implemented in ROS. Across simulations and real-robot experiments, the approach yields higher area coverage, improved map-quality metrics, and reduced computational load from frontier reduction, with asynchronous coordination providing enhanced robustness. The results demonstrate a scalable, centralized solution with potential for future decentralization and sensor-driven frontier candidate selection.

Abstract

In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present an efficient centralized frontier sharing approach that maximizes exploration by taking into account information gain in the merged map, distance, and reward computation among frontier candidates and encourages the spread of agents into the environment. Eventually, our method efficiently spreads the robots for maximum exploration while keeping SLAM uncertainty low. Additionally, we also present two coordination approaches, synchronous and asynchronous to prioritize robot goal assignments by the central server. The proposed method is implemented in ROS and evaluated through simulation and experiments on publicly available datasets and similar methods, rendering promising results.

Efficient Frontier Management for Collaborative Active SLAM

TL;DR

This work addresses the challenge of coordinating multiple robots in Active Collaborative SLAM by introducing a centralized frontier-sharing framework that optimizes exploration using merged-map information gain, distance, and reward metrics. The methodology combines frontier management to prune candidates, a spread policy to promote even exploration, and synchronous/asynchronous coordination modes implemented in ROS. Across simulations and real-robot experiments, the approach yields higher area coverage, improved map-quality metrics, and reduced computational load from frontier reduction, with asynchronous coordination providing enhanced robustness. The results demonstrate a scalable, centralized solution with potential for future decentralization and sensor-driven frontier candidate selection.

Abstract

In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present an efficient centralized frontier sharing approach that maximizes exploration by taking into account information gain in the merged map, distance, and reward computation among frontier candidates and encourages the spread of agents into the environment. Eventually, our method efficiently spreads the robots for maximum exploration while keeping SLAM uncertainty low. Additionally, we also present two coordination approaches, synchronous and asynchronous to prioritize robot goal assignments by the central server. The proposed method is implemented in ROS and evaluated through simulation and experiments on publicly available datasets and similar methods, rendering promising results.
Paper Structure (13 sections, 4 equations, 11 figures, 3 tables)

This paper contains 13 sections, 4 equations, 11 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Active SLAM
  4. Frontiers-based Approaches
  5. Active Collaborative SLAM
  6. METHODOLOGY
  7. Frontiers Management
  8. Spread Policy
  9. Sychronous and Asynchronous Approach
  10. RESULTS
  11. Simulation Results
  12. Experimental Results
  13. CONCLUSIONS

Figures (11)

  • Figure 1: Central server (red), and local nodes (yellow, green) communication.
  • Figure 2: The architecture of resultant system.
  • Figure 3: O.G map representing two points and their radius.
  • Figure 4: Environments used and the resulting O.G map showing the initial (red) and final positions (green) of robots.
  • Figure 5: % of map discovered in W.G and HOS Environments.
  • ...and 6 more figures