Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization
This addresses methane emission detection for environmental monitoring, but it is incremental as it builds on existing UAV-based sensing methods.
The study tackled the problem of localizing chemical plume sources from undocumented orphaned wells using UAVs, and the proposed multi-agent reinforcement learning framework achieved superior localization accuracy and operational efficiency compared to the fluxotaxis method.
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.