Slope-aware Adaptive Gaussian Process Sampling for Robotic Information Gathering on Rough Terrain

Wheeled robots are increasingly utilized for en- vironmental exploration and data collection in environments inaccessible to humans, such as the lunar surface. While adaptive sampling methods like the Upper Confidence Bound (UCB) can balance the trade-off between exploration and measurement data exploitation to model Regions of Interest (ROI), they do not explicitly incorporate robot traversability into the trade-off. Consequently, when faced with multiple scientifically promising locations, the UCB may select paths for the robot that are costly in terms of travel time, leading to time-inefficient surveys, particularly in sloped terrain. To address this limitation, we propose the Slope-aware Upper Confidence Bound (SaUCB), a novel acquisition function that integrates a traversability score based on terrain slope directly into the decision-making process. This allows the robot to explicitly balance exploration, exploitation, and traversal time. Through an extensive simulation study in realistic geological features of lunar terrain models, we demonstrate that the proposed approach generates significantly more time-efficient survey paths. Our method also demonstrates an improved trade-off between investigation time and modeling accuracy within the ROI compared to conventional approaches.