优化机器人集群巡逻路径与天线选择，提升隐蔽信号源定位精度。
Spatially Intelligent Patrol Routes for Concealed Emitter Localization by Robot Swarms
- 用差分进化生成几何巡逻路线，适应未知信号特性。
- 定向天线成功率达98.75%，定位误差仅1.01-1.30米。
- 适合需高精度电磁侦察的无人机/机器人集群系统设计。
本文提出一种基于差分进化的机器人集群巡逻路径设计方法，用于定位隐蔽无线电信号源。研究发现巡逻形状与天线类型显著影响信息增益，进而决定有效三角定位覆盖范围。通过四机器人集群在八种配置下的仿真测试，采用预生成路径，针对不同天线类型（全向或定向）及随机位置、功率、频率的信号源进行评估。结果表明，全向天线定位成功率主要受源位置影响，边缘区域失败率最高；而定向天线因更高增益和方向性，平均检测成功率达98.75%，显著优于全向天线的80.25%。定向传感平均定位误差为1.01–1.30米，全向为1.67–1.90米，且短边巡逻路径更优。研究表明，集群预测电磁现象的能力直接取决于其与环境的物理交互，因此通过优化巡逻路径与天线选型实现的空间智能，是高效机器人侦察的关键设计因素。
原文摘要 · Abstract (English)
This paper introduces a method for designing spatially intelligent robot swarm behaviors to localize concealed radio emitters. We use differential evolution to generate geometric patrol routes that localize unknown signals independently of emitter parameters, a key challenge in electromagnetic surveillance. Patrol shape and antenna type are shown to influence information gain, which in turn determines the effective triangulation coverage. We simulate a four-robot swarm across eight configurations, assigning pre-generated patrol routes based on a specified patrol shape and sensing capability (antenna type: omnidirectional or directional). An emitter is placed within the map for each trial, with randomized position, transmission power and frequency. Results show that omnidirectional localization success rates are driven primarily by source location rather than signal properties, with failures occurring most often when sources are placed in peripheral areas of the map. Directional antennas are able to overcome this limitation due to their higher gain and directivity, with an average detection success rate of 98.75% compared to 80.25% for omnidirectional. Average localization errors range from 1.01-1.30 m for directional sensing and 1.67-1.90 m for omnidirectional sensing; while directional sensing also benefits from shorter patrol edges. These results demonstrate that a swarm's ability to predict electromagnetic phenomena is directly dependent on its physical interaction with the environment. Consequently, spatial intelligence, realized here through optimized patrol routes and antenna selection, is a critical design consideration for effective robotic surveillance.
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