Deep Learning for Robotic Mass Transport Cloaking
Deep Learning for Robotic Mass Transport Cloaking
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DOI：
10.1109/tro.2020.2980176
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发表时间：
2018-12
影响因子：
7.8
通讯作者：
Reza Khodayi-mehr;M. Zavlanos
中科院分区：
文献类型：
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作者：
Reza Khodayi-mehr;M. Zavlanos
在这篇文章中，我们考虑使用移动的机器人的大规模运输隐形的问题。机器人沿着预定义的曲线移动，该曲线包围安全区并携带共同抵消环境中释放的化学试剂的源。目标是引导质量通量围绕所需区域，使得其不受外部浓度的影响。我们制定的问题，控制机器人的位置和释放率作为一个偏微分方程（PDE）约束的优化，其中的化学品的传播是由对流扩散（AD）PDE建模。我们使用一个神经网络（NN）来近似PDE的解决方案。特别是，我们提出了一种新的损失函数的神经网络，利用变分形式的AD-PDE，并允许我们重新规划问题作为一个无监督的基于模型的学习问题。我们的损失函数是离散化自由和高度并行化。与使用超材料来引导质量通量的被动隐身方法不同，我们的方法是第一个使用移动的机器人来主动控制浓度水平并创建独立于环境条件的安全区。我们证明了我们的方法在模拟的性能。
In this article, we consider the problem of mass transport cloaking using mobile robots. The robots move along a predefined curve that encloses a safe zone and carry sources that collectively counteract a chemical agent released in the environment. The goal is to steer the mass flux around a desired region so that it remains unaffected by the external concentration. We formulate the problem of controlling the robot positions and release rates as a partial differential equation (PDE)-constrained optimization, where the propagation of the chemical is modeled by the advection-diffusion (AD) PDE. We use a neural network (NN) to approximate the solution of the PDE. Particularly, we propose a novel loss function for the NN that utilizes the variational form of the AD-PDE and allows us to reformulate the planning problem as an unsupervised model-based learning problem. Our loss function is discretization-free and highly parallelizable. Unlike passive cloaking methods that use metamaterials to steer the mass flux, our method is the first to use mobile robots to actively control the concentration levels and create safe zones independent of environmental conditions. We demonstrate the performance of our method in simulations.