WetExplorer: Automating Wetland Greenhouse-Gas Surveys with an Autonomous Mobile Robot
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917079582507008 |
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| author | Vasquez, Jose Zhang, Xuping |
| author_facet | Vasquez, Jose Zhang, Xuping |
| contents | Quantifying greenhouse-gases (GHG) in wetlands is critical for climate modeling and restoration assessment, yet manual sampling is labor-intensive, and time demanding. We present WetExplorer, an autonomous tracked robot that automates the full GHG-sampling workflow. The robot system integrates low-ground-pressure locomotion, centimeter-accurate lift placement, dual-RTK sensor fusion, obstacle avoidance planning, and deep-learning perception in a containerized ROS2 stack. Outdoor trials verified that the sensor-fusion stack maintains a mean localization error of 1.71 cm, the vision module estimates object pose with 7 mm translational and 3° rotational accuracy, while indoor trials demonstrated that the full motion-planning pipeline positions the sampling chamber within a global tolerance of 70 mm while avoiding obstacles, all without human intervention. By eliminating the manual bottleneck, WetExplorer enables high-frequency, multi-site GHG measurements and opens the door for dense, long-duration datasets in saturated wetland terrain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10864 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WetExplorer: Automating Wetland Greenhouse-Gas Surveys with an Autonomous Mobile Robot Vasquez, Jose Zhang, Xuping Robotics Systems and Control Quantifying greenhouse-gases (GHG) in wetlands is critical for climate modeling and restoration assessment, yet manual sampling is labor-intensive, and time demanding. We present WetExplorer, an autonomous tracked robot that automates the full GHG-sampling workflow. The robot system integrates low-ground-pressure locomotion, centimeter-accurate lift placement, dual-RTK sensor fusion, obstacle avoidance planning, and deep-learning perception in a containerized ROS2 stack. Outdoor trials verified that the sensor-fusion stack maintains a mean localization error of 1.71 cm, the vision module estimates object pose with 7 mm translational and 3° rotational accuracy, while indoor trials demonstrated that the full motion-planning pipeline positions the sampling chamber within a global tolerance of 70 mm while avoiding obstacles, all without human intervention. By eliminating the manual bottleneck, WetExplorer enables high-frequency, multi-site GHG measurements and opens the door for dense, long-duration datasets in saturated wetland terrain. |
| title | WetExplorer: Automating Wetland Greenhouse-Gas Surveys with an Autonomous Mobile Robot |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2511.10864 |