Adaptive robot navigation protocol for estimating variable terrain elevation data

Efficiently measuring environmental phenomena (e.g., elevation, chemical composition, and mineral density) is a task typically reserved for the geoscience community. Recent robotic systems with the potential for addressing the task of sampling currently exist, yet their navigation strategies (and subsequently sampling strategies) are seldom a function of the spatial change in the measured phenomena of interest. Solutions are especially void for intelligent systems to which resource constraints are applied (i.e., battery power and experimentation time) while complete coverage of an area is expected. In this paper, we discuss the implementation of a custom navigation strategy based on immediately-sensed data that, when combined with spatial interpolation techniques, yields a re-creation of the surveyed space with root mean squared error that meets accepted mapping standards. Our methodology employs an adaptive coverage algorithm which succeeds in lowering the RMS error when compared to other navigation techniques. Our results are validated in simulation by considering: 1) randomly-generated terrains and 2) realistic digital elevation map (DEM) data transposed from publically available terrain contour maps.