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Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy

Khattiya Pongsirijinda, Zhiqiang Cao, Kaushik Bhowmik, Muhammad Shalihan, Billy Pik Lik Lau, Ran Liu, Chau Yuen, U-Xuan Tan

TL;DR

The paper tackles efficient distributed exploration in unknown environments by coupling a submap-based multi-robot mapping pipeline (DSMC-Map) with a noise-augmented potential-field exploration strategy (MWF-CN). DSMC-Map merges local submaps via loop closures and distributed pose-graph optimization to yield a consistent global map, mitigating drift and central bottlenecks. The MWF-CN strategy extends frontier neighborhoods with a Modified Wave-Front distance and injects colored noise into inter-robot repulsion to diversify exploration paths and reduce local optima. Across simulated and real-world experiments, the approach achieves faster exploration, better inter-robot collaboration, and higher map quality than state-of-the-art baselines, demonstrating practical robustness for scalable multi-robot deployment.

Abstract

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.

Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy

TL;DR

The paper tackles efficient distributed exploration in unknown environments by coupling a submap-based multi-robot mapping pipeline (DSMC-Map) with a noise-augmented potential-field exploration strategy (MWF-CN). DSMC-Map merges local submaps via loop closures and distributed pose-graph optimization to yield a consistent global map, mitigating drift and central bottlenecks. The MWF-CN strategy extends frontier neighborhoods with a Modified Wave-Front distance and injects colored noise into inter-robot repulsion to diversify exploration paths and reduce local optima. Across simulated and real-world experiments, the approach achieves faster exploration, better inter-robot collaboration, and higher map quality than state-of-the-art baselines, demonstrating practical robustness for scalable multi-robot deployment.

Abstract

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.
Paper Structure (17 sections, 11 equations, 11 figures, 2 tables, 2 algorithms)

This paper contains 17 sections, 11 equations, 11 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. DSMC-Map mapping strategy
  3. Submap construction
  4. Loop closure detection
  5. Distributed pose graph optimization
  6. MWF-CN exploration strategy
  7. Attractive potential of frontiers
  8. Repulsive potential of robots
  9. Simulation
  10. Evaluation metrics
  11. Parameter adjustment for the fastest exploration time
  12. Benchmark and comparison
  13. Computation time analysis
  14. Real-world deployment
  15. Hardware and experimental setup
  16. ...and 2 more sections

Figures (11)

  • Figure 1: Illustration of the DMPF-Explore with distributed mapping and exploration running on the individual robot. The map in Fig. 1(b) is built by Robot 1 in Fig. 1(a), and the map in Fig. 1(d) is built by Robot 2 in Fig. 1(c).
  • Figure 2: Overview of the DMPF-Explore framework
  • Figure 3: Graphical representation of the exploration by MWF-CN from robot1's point of view. $P_\text{total}$ is the total potential calculated by eq. (2), $P_r$ is the repulsive potential calculated by eq. (9), and the green arrow is the robot1's desired direction to reach the goal at the centroid with the lowest $P_\text{total}$.
  • Figure 4: Examples of the calculation by different types of wave-front distances
  • Figure 5: Example of noises generated by different noise colors $\alpha$ and variances $\sigma_d$ with the sampling rate at 1 Hz
  • ...and 6 more figures