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

2025-03-14

AI Summary

This paper addresses the distributed multi-robot source-seeking problem in unknown environments where the number of sources is unknown and may exceed the number of robots. To tackle this challenge, we propose DIAS—a novel framework that integrates source existence detection with dynamic exploration–exploitation switching, synergistically combining Voronoi-based spatial partitioning and Gaussian process regression (GPR). DIAS enables global source discovery and continuous density function modeling without prior knowledge of source count. It is fully distributed, robot-autonomous, and compatible with existing source-seeking algorithms. In gas-leakage simulation benchmarks, DIAS significantly outperforms baseline methods—including DoSS and GMES—in both source identification efficiency and density estimation accuracy. Experimental results demonstrate DIAS’s effectiveness and robustness in complex, unstructured environments, establishing its capability for reliable source sensing and probabilistic environmental modeling under severe uncertainty.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develops a distributed framework for multi-robot source seeking in unknown environments.
• Addresses challenges with unknown and potentially numerous sources.
• Enhances search efficiency using Voronoi cells and Gaussian process regression.

Innovation

• Hybrid controller for exploration and exploitation
• Voronoi cells for environment division
• Gaussian process regression for density approximation