巡逻机器人群中通过高斯信念传播进行环境信号的集体排序

中文摘要 AI 辅助

多机器人巡逻需要一组机器人定期访问环境中的所有区域，通常以最小化空闲时间为目标。出于安全和环境监测的动机，一个实际扩展是根据测量信号对所有巡逻位置形成集体排序，这是最佳n问题向多选项、连续值领域的推广。我们观察到巡逻图具有自然的双重解释：它既是决定智能体移动的拓扑结构，又是可传播空间信念的因子图。利用这种等价性，我们应用基于图的算法高斯信念传播（GBP），通过已访问节点的一元测量因子和巡逻边的成对平滑因子来进行集体排序。我们在仿真中，在一系列传感器噪声条件下将GBP与简单平均和访问计数加权平均进行比较，并在四个Leo Rover机器人跟踪办公室大厅中传播的无线电信号的实验中验证该方法。GBP在排序准确性、均方误差和达成共识的时间方面均优于两种基准方法。我们发现，随着噪声增加和任务难度提升，GBP在仿真中性能平稳下降，而两种平均方法则大幅下降。硬件实验在真实传播的无线电信号上重现了相同的性能排序，支持了仿真结果的实际相关性。

英文摘要

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.

发表机构

• University of Bristol（布里斯托尔大学）

机构由 AI 辅助整理，请以论文原文为准。