Rapid coverage of regions of interest for environmental monitoring

We present a framework for rapidly determining regions of interest (ROIs) from an unknown intensity distribution, particularly in radiation fields. The vast majority of studies on area coverage path planning for mobile robots do not investigate the identification of ROIs. In a radiation field, the use of ROIs can limit the required range of exploration and mitigate the monitoring problem. However, considering that an unmanned aerial vehicle (UAV) has limited resources as a mobile measurement system, it is challenging to determine ROIs in unknown radiation fields. Given a target area, we attempt to plan a path that facilitates the localization of ROIs with a single UAV while minimizing the exploration cost. To reduce the complexity of a large-scale environment exploration, entire areas are initially adaptively decomposed using two hierarchical methods based on recursive quadratic subdivision and Voronoi-based subdivision. Once an informative decomposed subarea is selected by maximizing a utility function, the robot heuristically reaches contaminated areas, and a boundary estimation algorithm is adopted to estimate the environmental boundaries. The properties of this boundary estimation algorithm are theoretically analyzed in this paper. Finally, the detailed boundaries of the ROIs of the target area are approximated by ellipses, and a set of procedures are iterated to sequentially cover all areas. The simulation results demonstrate that our framework allows a single UAV to efficiently explore a given target area and maximize the localization rate for ROIs.