SOURCE-LINKED INTELLIGENCE

Gaussian Processes for Modelling Spatial Fields with Robot Swarms

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Robot swarms, by virtue of their decentralised architecture, are a natural tool for scalable, robust modelling of spatial fields, such as water temperature, wind velocity, or terrain elevation. However, existing methods rely on external positioning systems that allow each robot to determine its own position in space. Here, we introduce location-unaware Gaussian process regression (LU-GPR) as a solution to the modelling of spatial fields in the absence of such positioning systems. LU-GPR allows each robot to infer the posterior mean and variance of the field in space, while simultaneously agree

Read original source ↗ Open in workspace

recordType

paper

region

Global