Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents.

ABSTRACT: There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi's orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learnin

SUBMITTER: Husnain AU 

PROVIDER: S-EPMC10889584 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature