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.
Note to Practitioners
—This article was motivated by the need for a practical and systematic approach for reconstruction and planning to execute robotic active sensing (AS) in environmental field mapping. Field robotic applications are not maneuverable in comparison with indoor scenarios due to severe conflict between the need for long execution endurance in the field and the very limited onboard resources. Traditional AS planners normally use statistical model-based informative metrics, which may lead to model misspecification in real-world phenomena. The developed framework in this article yields a novel attention-driven metric to guide AS and mapping. It relies on an attention-based hybrid neural network that reveals the driving variables in terms of the heterogeneities and complexities in a natural environment. The high-priority regions are maximized in a coverage path depending on the inferred spatial attention distribution while maintaining the travel cost of the sensing robots within an available energy budget. Experiments using a remote sensing dataset validate the reliable performance of the proposed framework, in environmental field mapping.