Path to autonomous soil sampling and analysis by ground-based robots☆

被引:0
作者
Norby, Joe
Wang, Sean
Wang, Hairong
Deng, Shane
Jones, Nick
Mishra, Akshit
Pavlov, Catherine
He, Hannah [1 ]
Subramanian, Sathya
Thangavelu, Vivek
Sihota, Natasha [2 ]
Hoelen, Thomas [2 ]
Johnson, Aaron M.
Lowry, Gregory V.
机构
[1] Carnegie Mellon Univ, Comp Sci Dept, Pittsburgh, PA USA
[2] Chevron Technol Ctr, Richmond, CA USA
关键词
Remediation; Robot platform; Site characterization; Sensors; Environmental Sampling; Autonomy; HYDROCARBON-CONTAMINATED SOIL; MOBILE ROBOTS; EXCAVATION; SYSTEMS; FORCES;
D O I
10.1016/j.jenvman.2024.121130
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis. However, recent advances in terrestrial robotics and artificial intelligence offer a potentially revolutionary set of tools and methods that will help to autonomously explore natural environments, select sample locations with the highest value of information, extract samples, and analyze the data in real-time without exposing humans to potentially hazardous conditions. A fundamental challenge to realizing this potential is determining how to design an autonomous system for a given investigation with many, and often conflicting design criteria. This work presents a novel design methodology to navigate these criteria. Specifically, this methodology breaks the system into four components - sensing, sampling, mobility, and autonomy - and connects design variables to the investigation objectives and constraints. These connections are established for each component through a survey of existing technology, discussion of key technical challenges, and highlighting conditions where generality can promote multi-application deployment. An illustrative example of this design process is presented for the development and deployment of a robotic platform characterizing salt-impacted oil & gas reserve pits. After calibration, the relationship between the in situ robot chloride measurements and laboratory-based chloride measurements had a good linear relationship (R2-value = 0.861) and statistical significance (p-value = 0.003).
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页数:9
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