Collective Ranking of Environmental Signals through Gaussian Belief Propagation in a Patrolling Robot Swarm

2026-08-18

AI Summary

研究使用高斯信念传播算法解决多机器人巡逻中环境信号集体排序问题，通过图拓扑和因子图的双重解释，提高在不同噪声条件下的排名准确性。

Abstract

Multi-robot patrolling requires a team to visit all areas of an environment at regular intervals, typically minimising idleness. A practical extension, motivated by security and environmental monitoring, is to additionally form a collective ranking of all patrol locations by some measured signal, a generalisation of the best-of-n problem to the many-option, continuous-valued regime. We observe that the patrol graph admits a natural dual interpretation: it is simultaneously the topology that dictates agent movement and a factor graph over which spatial beliefs can be propagated. Exploiting this equivalence, we apply Gaussian Belief Propagation (GBP), a graph-based algorithm, to collective ranking using unary measurement factors at visited nodes and pairwise smoothness factors along patrol edges. We compare GBP against simple and visit-count-weighted averaging across a range of sensor-noise conditions in simulation, and validate the approach on four Leo Rovers tracking a propagating radio signal in an office lobby. GBP outperforms both baselines on ranking accuracy, mean squared error, and time to consensus. We find that as noise increases and the task becomes harder, GBP degrades gracefully in simulation while both averaging methods degrade substantially. Hardware trials reproduce the same performance ordering on a real propagating radio signal, supporting the practical relevance of the simulated results.

Problem

• Multi-robot patrolling
• collective ranking
• Gaussian Belief Propagation
• patrol graph
• sensor-noise

Innovation

• Gaussian Belief Propagation
• Collective Ranking
• Patrol Graph
• Spatial Beliefs
• Robustness to Noise