2026-05-27原文

Collaborative Navigation and Exploration with β-Sparse Gaussian Processes

A framework for heterogeneous robot collaboration under bandwidth constraints, using β-Sparse Gaussian Processes for task-aware point selection and balancing exploration, achieving 18% path cost reduction and 76% information reduction in simulations.

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Key points

Novel β-Sparse Gaussian Process model for task-aware inducing point selection
Online joint selection of map points and navigation actions by sensor robot
Action-selection strategy balancing task relevance and exploration
Simulations show 18% path cost reduction and 76% information reduction

Why it matters

This matters because novel β-Sparse Gaussian Process model for task-aware inducing point selection.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2605.26304] Collaborative Navigation and Exploration with $β$-Sparse Gaussian Processes

[Submitted on 25 May 2026]

Title:Collaborative Navigation and Exploration with $β$-Sparse Gaussian Processes

View a PDF of the paper titled Collaborative Navigation and Exploration with $\beta$-Sparse Gaussian Processes, by Evangelos Psomiadis and 2 other authors

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Abstract:Collaborative navigation of heterogeneous robots in unknown environments poses significant challenges due to sensing, communication, and computational limitations. In this work, a lead robot navigates toward a target while a mobile sensor robot (e.g., a drone) assists by transmitting information about its locally observed environment under bandwidth constraints. We propose a framework that enables the sensor to jointly select its transmitted map points and navigation actions online, while also predicting unexplored regions of the environment. To this end, we present $\beta$-Sparse Gaussian Processes, a novel and robust variational sparse Gaussian Process model for task-aware inducing point selection. Furthermore, we develop an action-selection strategy that balances task relevance with exploration. Simulations on Mars and Earth maps show that the framework can reduce path cost by 18% relative to no communication and decrease transmitted information by 76% compared to raw-data transmission baselines.

Comments: 16 pages, 6 figures

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2605.26304 [cs.RO]

(or arXiv:2605.26304v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.26304

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Evangelos Psomiadis [view email] [v1] Mon, 25 May 2026 19:55:59 UTC (4,886 KB)

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