Attention-Driven Active Sensing With Hybrid Neural Network for Environmental Field Mapping

In environmental monitoring programs, mobile robots have been widely deployed for remote sensing, with the end objective of monitoring and mapping out environmental fields. Complex characteristics and correlations in natural phenomena make it challenging to establish a reliable framework for mobile sensing and field mapping. Furthermore, constraints of onboard resources will limit the ability of mobile robots to cover a large area. This article focuses on the active sensing problem in environmental field mapping and particularly exploits the use of intrinsic interactions among multivariate spatiotemporal data. A novel deep neural network of a hybrid CNN-RNN model is employed to learn the monitored multivariate spatiotemporal field. Specifically, a set of attention mechanisms is designed and embedded in the network, which is able to adaptively capture parameterwise dependencies among the monitored heterogeneous parameters and spatial correlations in geolocations of a surveyed field. The weights of inferred attention facilitate explicit interpretation of the driving parameters and geolocations. Some subregions of interest in the surveyed field are specified by their spatial attention distribution and are actively sensed by following the proposed coverage path planner. Experiments are carried out using a real-world dataset with multisource environmental imagery from a remote sensing program. Experimental results are obtained, which demonstrate the superior mapping performance of the proposed systematical methodology compared to baseline methods. Furthermore, the proposed model is able to quantitatively reveal the driving monitored parameters and geolocations in a regression process.