Gaussian Processes for Modelling Spatial Fields with Robot Swarms

• URL: http://arxiv.org/abs/2609.17463v1
• Date: Tue, 15 Sep 2026 17:03:11 GMT
• Title: Gaussian Processes for Modelling Spatial Fields with Robot Swarms
• Abstract summary: We introduce location-unaware Gaussian process regression (LU-GPR) as a solution to the modelling of spatial fields.<n>LU-GPR allows each robot to infer the posterior mean and variance of the field in space, while simultaneously agreeing on a common frame of reference with its peers.<n>Our results show that LU-GPR scales well with the number of robots and is robust to limited communication ranges.
• Score: 9.178969775955185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 agreeing on a common frame of reference with its peers, using only local sensing and communication. We propose an online algorithm that allows each robot to consistently infer local estimates as its local frame of reference converges to the common one. By means of a product of experts model, each robot also combines the estimates of its peers with its own to obtain a global model. Our results show that LU-GPR scales well with the number of robots and is robust to limited communication ranges. We also demonstrate how it can be used in real-world monitoring scenarios to estimate the flow of an evacuating crowd.

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