Environmental monitoring with mobile robots

This paper describes an architecture for estimating environmental odor maps and presents experimental results obtained using that architecture with five mobile robots inside a large laboratory with two odor sources and forced ventilation. The mobile sensing agents employed in the experiments have self localization capabilities and carry an electronic nose and a thermal anemometer. The proposed architecture allows integrating sparse olfaction data gathered by the mobile agents along their trajectories and dynamically estimate the spatial concentration of different odor fields. The data assimilation process is made centrally by a PC that polls periodically each robot through a RF network in order to get the data gathered during the previous acquisition period. The estimation of odor fields is made in two steps: first each agent estimates the odor mixture and concentration by means of a neural network-based regression algorithm that converts values from gas sensor space to the corresponding odor space. Then the sensed data is assimilated into an advection-diffusion model by means of a reduced order Kalman filter. In the current implementation the central controller, responsible for the maps estimation, specifies to each agent their target area to explore. The proposed architecture was validated with a set of experiments that demonstrated its ability to estimate and capture the dynamics of overlapping odor fields. This work can easily be adapted to city buses or other local transportation systems in order to monitor the pollution or quickly detect hazardous chemicals inside cities.