Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912963089137664 |
|---|---|
| author | Li, Zhirun Hollenbeck, Derek Wu, Ruikun Sherman, Michelle Shao, Sihua Sun, Xiang Hassanalian, Mostafa |
| author_facet | Li, Zhirun Hollenbeck, Derek Wu, Ruikun Sherman, Michelle Shao, Sihua Sun, Xiang Hassanalian, Mostafa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11582 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization Li, Zhirun Hollenbeck, Derek Wu, Ruikun Sherman, Michelle Shao, Sihua Sun, Xiang Hassanalian, Mostafa Systems and Control Multiagent Systems 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. |
| title | Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization |
| topic | Systems and Control Multiagent Systems |
| url | https://arxiv.org/abs/2603.11582 |