Model-Based Active Source Identification in Complex Environments
Model-Based Active Source Identification in Complex Environments
复制标题
复杂环境中基于模型的主动源识别
DOI：
10.1109/tro.2019.2894039
复制
发表时间：
2017
影响因子：
7.8
通讯作者：
M. Zavlanos
中科院分区：
文献类型：
--
作者：
Reza Khodayi;W. Aquino;M. Zavlanos
本文研究了稳态对流扩散输运系统中的有源识别问题。与现有的生物启发式方法不同，我们提出了一种基于模型的方法，采用AD-偏微分方程（PDE）来捕捉运输现象。具体来说，我们制定的源识别（SI）问题作为一个偏微分方程约束的函数空间中的优化问题。为了获得一个易于处理的解决方案，我们降低了使用适当的正交分解和近似的未知源场的浓度场的维数，使用非线性基函数，大大减少了未知数的数量。此外，收集的浓度测量，我们控制机器人传感器通过一系列的路点，最大限度地提高未知源参数的Fisher信息矩阵的最小特征值。具体地，在每个新的测量之后，解决SI问题以获得用于确定下一个航路点的源估计。我们表明，我们的算法可以有效地识别源在复杂的AD系统和非凸域，在模拟和实验。这是偏微分方程首次用于机器人SI的实践。
In this paper, we consider the problem of Active Source Identification in steady-state advection–diffusion (AD) transport systems. Unlike existing bioinspired heuristic methods, we propose a model-based approach that employs the AD–partial differential equation (PDE) to capture the transport phenomenon. Specifically, we formulate the source identification (SI) problem as a PDE-constrained optimization problem in function spaces. To obtain a tractable solution, we reduce the dimension of the concentration field using Proper Orthogonal Decomposition and approximate the unknown source field using nonlinear basis functions, drastically decreasing the number of unknowns. Moreover, to collect the concentration measurements, we control a robot sensor through a sequence of waypoints that maximize the smallest eigenvalue of the Fisher Information matrix of the unknown source parameters. Specifically, after every new measurement, an SI problem is solved to obtain a source estimate that is used to determine the next waypoint. We show that our algorithm can efficiently identify sources in complex AD systems and nonconvex domains, in simulation and experimentally. This is the first time that PDEs are used for robotic SI in practice.