Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization

2026-03-12

AI Summary

This study addresses the challenge of efficiently detecting and localizing toxic gases, such as methane, emanating from abandoned wellheads—a task poorly suited to conventional methods. To this end, the authors propose a multi-agent deep reinforcement learning (MARL)-based framework for cooperative drone sensing. The approach introduces a virtual anchor mechanism to coordinate multiple unmanned aerial vehicles in conducting simultaneous in situ measurements of gas concentration and wind velocity. By analyzing historical trajectories of these virtual anchors, the system achieves precise source localization. Compared to traditional fluxotaxis-based techniques, the proposed framework demonstrates significant improvements in both localization accuracy and operational efficiency, offering a scalable and effective solution for gas leak monitoring in complex environments.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Undocumented orphaned wells pose significant health and environmental risks to nearby communities by releasing toxic gases and contaminating water sources, with methane emissions being a primary concern. Traditional survey methods such as magnetometry often fail to detect older wells effectively. In contrast, aerial in-situ sensing using unmanned aerial vehicles (UAVs) offers a promising alternative for methane emission detection and source localization. This study presents a robust and efficient framework based on a multi-agent deep reinforcement learning (MARL) algorithm for the chemical plume source localization (CPSL) problem. The proposed approach leverages virtual anchor nodes to coordinate UAV navigation, enabling collaborative sensing of gas concentrations and wind velocities through onboard and shared measurements. Source identification is achieved by analyzing the historical trajectory of anchor node placements within the plume. Comparative evaluations against the fluxotaxis method demonstrate that the MARL framework achieves superior performance in both localization accuracy and operational efficiency.

Problem

• chemical plume source localization
• orphaned wells
• methane emissions
• UAV-based sensing
• multi-agent reinforcement learning

Innovation

• multi-agent reinforcement learning
• UAV-based sensing
• chemical plume source localization
• virtual anchor nodes
• collaborative navigation