基于估计和信息驱动改进方向的切换式回合自适应源搜寻策略

中文摘要 AI 辅助

源搜寻应用于气体泄漏定位、辐射监测、环境监控等场景，需通过空间测量估算未知信号场的源位置。实际中源位置无法直接观测，需从机器人运动过程中采集的含噪标量测量值推断。在机器人源搜寻中，估计与运动紧密关联：测量可提升源估计精度，而所选轨迹会影响后续测量质量。现有基于循环的几何策略虽能生成可行运动，但未明确利用估计不确定性调控运动方向。本文提出一种基于循环的源搜寻框架，将扩展卡尔曼滤波（EKF）估计与基于费舍尔信息矩阵（FIM）的方向选择相结合：运动过程中更新源估计，在循环边界处同时利用估计不确定性与预测信息增益调整航向；采用基于测量的停止条件检测收敛，无需源的先验知识。在静止和移动源场景下的结果表明，与纯信息驱动或纯估计驱动的策略相比，该策略的跟踪性能更优，估计误差更小。

英文摘要

Source seeking arises in applications such as gas leak localization, radiation monitoring, and environmental surveillance, where the origin of an unknown signal field must be estimated from spatial measurements. In practice, the source location is not directly observable and must be inferred from noisy scalar measurements collected during motion.In robotic source seeking, estimation and motion are closely linked: measurements improve the source estimate, while the chosen trajectory affects the quality of future measurements.Existing loop-based geometric strategies generate feasible motion but do not explicitly use estimation uncertainty to regulate direction updates.This paper presents a loop-based source-seeking framework that combines Extended Kalman Filter (EKF) estimation with Fisher Information Matrix (FIM)-based direction selection. The source estimate is updated during motion, and the heading is changed at loop boundaries using both estimation uncertainty and predicted information gain. A measurement-based stopping condition is used to detect convergence without requiring prior knowledge of the source location.Simulation results under stationary and moving source scenarios demonstrate improved tracking performance and reduced estimation error compared to purely information-driven or estimate-driven strategies.

发表机构

• Indian Institute of Technology Madras（印度马德拉斯理工学院）

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