Deep Learning for Robotic Mass Transport Cloaking

12/11/2018
by   Reza Khodayi-mehr, et al.
0

We consider the problem of Mass Transport Cloaking using mobile robots. The robots 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 PDE-constrained optimization, where the propagation of the chemical is modeled by the Advection-Diffusion (AD) PDE. Since discretization-based methods, such as the Finite Element method, are computationally demanding for time-dependent problems, we use a Deep 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.

READ FULL TEXT
research
12/16/2019

VarNet: Variational Neural Networks for the Solution of Partial Differential Equations

In this paper we propose a new model-based unsupervised learning method,...
research
11/29/2017

PDE-Based Optimization for Stochastic Mapping and Coverage Strategies using Robotic Ensembles

This paper presents a novel partial differential equation (PDE)-based fr...
research
03/21/2021

A variational interpretation of Restricted Additive Schwarz with impedance transmission condition for the Helmholtz problem

In this paper we revisit the Restricted Additive Schwarz method for solv...
research
01/04/2021

Hybrid FEM-NN models: Combining artificial neural networks with the finite element method

We present a methodology combining neural networks with physical princip...
research
12/06/2022

Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots

Flexible robots may overcome the industry's major problems: safe human-r...
research
08/24/2021

Discretization of parameter identification in PDEs using Neural Networks

We consider the ill-posed inverse problem of identifying parameters in a...

Please sign up or login with your details

Forgot password? Click here to reset