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Distributed Multi-robot Source Seeking in Unknown Environments with Unknown Number of Sources

Lingpeng Chen, Siva Kailas, Srujan Deolasee, Wenhao Luo, Katia Sycara, Woojun Kim

TL;DR

DIAS addresses multi-robot source seeking when the number of sources is unknown and may exceed the number of robots. It combines Voronoi-based environment partitioning, GP-based density estimation, and a hybrid exploration–exploitation controller to identify all potential sources and to adaptively switch between active sensing and source seeking. The approach yields faster and more accurate source detection and environmental density estimation than baselines such as DoSS and GMES, and can be integrated with existing source-seeking algorithms to further improve performance. The framework holds practical value for rapid, collaborative environmental monitoring and hazard localization in uncertain settings.

Abstract

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

Distributed Multi-robot Source Seeking in Unknown Environments with Unknown Number of Sources

TL;DR

DIAS addresses multi-robot source seeking when the number of sources is unknown and may exceed the number of robots. It combines Voronoi-based environment partitioning, GP-based density estimation, and a hybrid exploration–exploitation controller to identify all potential sources and to adaptively switch between active sensing and source seeking. The approach yields faster and more accurate source detection and environmental density estimation than baselines such as DoSS and GMES, and can be integrated with existing source-seeking algorithms to further improve performance. The framework holds practical value for rapid, collaborative environmental monitoring and hazard localization in uncertain settings.

Abstract

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

Paper Structure

This paper contains 17 sections, 11 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Problem Setup
  4. Gaussian Process Regression
  5. Ergodic Control
  6. Proposed Algorithm
  7. Environment Estimation
  8. Hybrid Controller
  9. Potential source identification module
  10. Source seeking module
  11. Active sensing module
  12. Experimental Results
  13. Simulated Environment Setup
  14. Performance Comparison
  15. Trajectory Analysis
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Overview: three robots navigate in a field to identify all potential sources. The upper layer shows the distribution of the gas concentration level. The lower layer depicts the unexplored 2D environment with a projected heat map of the distribution. The black dashed lines represent Voronoi partition boundaries.
  • Figure 2: Comparison of the Weighted Root Mean Squared Error (WRMSE) trends throughout the different methods across 3 sources (left), 5 sources (middle), and 7 sources (right) scenarios. A faster decrease in the WRMSE value suggests a more rapid convergence of the estimated distribution $\mu(\cdot)$ towards the ground truth distribution $\phi(\cdot)$.
  • Figure 3: Illustration of three robots searching for five static chemical leakage sources in an unknown area. Each row depicts the trajectory of the robots using DIAS and GreedyBO strategies at $30$, $45$, and $60$ iterations. DIAS successfully identifies all sources within $51$ iterations, whereas GreedyBO requires $106$ iterations.