This article presents a new decentralized multi-agent information-theoretic (DeMAIT) control algorithm for mobile sensors (agents). The algorithm leverages Bayesian estimation and information-theoretic motion planning for efficient and effective estimation and localization of a target, such as a chemical gas leak. The algorithm consists of: (1) a non-parametric Bayesian estimator, (2) an information-theoretic trajectory planner that generates “informative trajectories” for agents to follow, and (3) a controller and collision avoidance algorithm to ensure that each agent follows its trajectory as closely as possible in a safe manner. Advances include the use of a new information-gain metric and its analytical gradient, which do not depend on an infinite series like prior information metrics. Dynamic programming and multi-threading techniques are applied to efficiently compute the mutual information to minimize measurement uncertainty. The estimation and motion planning processes also take into account the dynamics of the sensors and agents. Extensive simulations are conducted to compare the performance between the DeMAIT algorithm to a traditional raster-scanning method and a clustering method with coordination. The main hypothesis that the DeMAIT algorithm outperforms the other two methods is validated, specifically where the average localization success rate for the DeMAIT algorithm is (a) higher and (b) more robust to changes in the source location, robot team size, and search area size than the raster-scanning and clustering methods. Finally, outdoor field experiments are conducted using a team of custom-built aerial robots equipped with gas concentration sensors to demonstrate efficacy of the DeMAIT algorithm to estimate and find the source of a propane gas leak.

中文翻译：

---

用于目标估计和定位的分散式多智能体信息论控制：查找气体泄漏

本文介绍了一种用于移动传感器（代理）的新型分散式多代理信息理论 (DeMAIT) 控制算法。该算法利用贝叶斯估计和信息论运动规划来有效地估计和定位目标，例如化学气体泄漏。该算法包括：(1) 非参数贝叶斯估计器，(2) 生成“信息轨迹”供代理遵循的信息理论轨迹规划器，以及 (3) 控制器和避免碰撞算法，以确保每个代理以安全的方式尽可能接近其轨迹。进步包括使用新的信息增益度量及其分析梯度，它不依赖于像先验信息度量那样的无限序列。应用动态编程和多线程技术来有效地计算互信息以最小化测量不确定性。估计和运动规划过程还考虑了传感器和代理的动态。进行了广泛的模拟以比较 DeMAIT 算法与传统光栅扫描方法和具有协调的聚类方法之间的性能。DeMAIT 算法优于其他两种方法的主要假设得到验证，特别是在 DeMAIT 算法的平均定位成功率（a）更高和（b）对源位置、机器人团队规模和搜索的变化更稳健的情况下区域大小比光栅扫描和聚类方法。最后，