外观
论文arXiv 2608.176902 个月前
巡逻机器人群体中通过 Gaussian Belief Propagation 对环境信号进行集体排序
原文链接：Collective Ranking of Environmental Signals through Gaussian Belief Propagation in a Patrolling Robot Swarm（arXiv 2608.17690 · 发布日期：2026-08-18）
Collective Ranking of Environmental Signals through Gaussian Belief Propagation in a Patrolling Robot Swarm
针对多机器人巡逻中的环境信号集体排序，利用巡逻图上 GBP 传播信念，并在仿真和四台 Leo Rovers 实测中验证。
核心要点
- 多机器人巡逻除降低空闲时间外，还需按测量信号对所有巡逻位置形成集体排序。
- 作者将巡逻图同时视为运动拓扑和因子图，使用 GBP 融合访问节点测量与边上的平滑约束。
- 在不同传感器噪声仿真中，GBP 在排序准确率、均方误差和达成共识时间上优于两种平均基线。
- 真机试验在办公室大厅中用四台 Leo Rovers 跟踪传播无线电信号，复现了相同性能排序。
验证范围与局限：验证范围包括仿真中的多种传感器噪声条件，以及四台 Leo Rovers 在办公室大厅跟踪传播无线电信号的真机试验。
英文摘要 (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.
作者：Zachary R. Madin, Connor York, Jonathan Lawry B, Edmund R. Hunt
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