论文标题
基于分布式信息的来源寻求
Distributed Information-based Source Seeking
论文作者
论文摘要
在本文中，我们设计了一个基于信息的多机器人来源，以寻求算法，其中一组移动传感器仅使用基于局部范围的测量值将一组移动传感器本地化和移动靠近单个源。在算法中，移动传感器执行源标识/定位以估计源位置；同时，他们移至新位置，以最大程度地提高有关传感器测量中包含的源的Fisher信息。在这样做的过程中，它们改善了源位置的估计，并更靠近源。与传统的攀登算法相比，我们的算法在收敛速度上具有优越性，在测量模型和信息指标的选择中是灵活的，并且对测量模型误差非常健壮。此外，我们提供了算法的完全分布式版本，每个传感器都会决定自己的动作，仅通过稀疏的通信网络与邻居共享信息。我们进行密集的仿真实验，以测试带有光传感器的小型地面车辆上的大型系统和物理实验的算法，这表明在寻求光源方面取得了成功。
In this paper, we design an information-based multi-robot source seeking algorithm where a group of mobile sensors localizes and moves close to a single source using only local range-based measurements. In the algorithm, the mobile sensors perform source identification/localization to estimate the source location; meanwhile, they move to new locations to maximize the Fisher information about the source contained in the sensor measurements. In doing so, they improve the source location estimate and move closer to the source. Our algorithm is superior in convergence speed compared with traditional field climbing algorithms, is flexible in the measurement model and the choice of information metric, and is robust to measurement model errors. Moreover, we provide a fully distributed version of our algorithm, where each sensor decides its own actions and only shares information with its neighbors through a sparse communication network. We perform intensive simulation experiments to test our algorithms on large-scale systems and physical experiments on small ground vehicles with light sensors, demonstrating success in seeking a light source.
