Informative path planning for gas distribution mapping in cluttered environments

Mobile robotic gas distribution mapping (GDM) is a useful tool for hazardous scene assessment where a quick and accurate representation of gas concentration levels is required throughout a staging area. However, research in robotic path planning for GDM has primarily focused on mapping in open spaces or estimating the source term in dispersion models. Whilst this may be appropriate for environment monitoring in general, the vast majority of GDM applications involve obstacles, and path planning for autonomous robots must account for this. This paper aims to tackle this challenge by integrating a GDM function with an informative path planning framework. Several GDM methods are explored for their suitability in cluttered environments and the GMRF method is chosen due to its ability to account for obstacle interactions within the plume. Based on the outputs of the GMRF, several reward functions are proposed for the informative path planner. These functions are compared to a lawnmower sweep in a high fidelity simulation, where the RMSE of the modelled gas distribution is recorded over time. It is found that informing the robot with uncertainty, normalised concentration and time cost, significantly reduces the time required for a single robot to achieve an accurate map in a large-scale, urban environment. In the context of a hazardous gas release scenario, this time reduction could save lives as well as further gas ingress.