Distributed Information-Based Source Seeking
Distributed Information-Based Source Seeking
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DOI：
10.1109/tro.2023.3309099
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发表时间：
2022-09
影响因子：
7.8
通讯作者：
Tianpeng Zhang;Victor Qin;Yujie Tang;Na Li
中科院分区：
文献类型：
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作者：
Tianpeng Zhang;Victor Qin;Yujie Tang;Na Li
在本文中，我们设计了一种基于信息的多机器人源搜索算法，其中一组移动传感器仅使用基于局部范围的测量来定位并移动到单个源附近。在该算法中，移动传感器进行源识别/定位以估计源位置；与此同时，他们移动到新的地点，以最大化有关传感器测量中包含的源的费舍尔信息。通过这样做，他们改进了源位置估计并更接近源。与传统的爬场算法相比，我们的算法收敛速度优越，测量模型和信息度量的选择灵活，对测量模型误差具有鲁棒性。此外，我们提供了算法的完全分布式版本，其中每个传感器决定自己的操作，并且仅通过稀疏通信网络与其邻居共享信息。我们进行了大量的模拟实验，在大型系统上测试我们的算法，并在带有光传感器的小型地面车辆上进行物理实验，证明了寻找光源的成功。
In this article, we design an information-based multirobot 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 extensive simulation experiments to test our algorithms on large-scale systems and implement physical experiments on small ground vehicles with light sensors, demonstrating success in seeking a light source.