# Deep Learning for Robotic Mass Transport Cloaking

## TL;DR

This paper introduces a novel neural network-based approach for controlling mobile robots to actively create safe zones by cloaking chemical agents, using PDE-constrained optimization and a variational loss function, demonstrated through simulations.

## Contribution

It presents the first method to actively control chemical flux with mobile robots using a PDE-based neural network approach and a novel, discretization-free loss function.

## Key findings

- Effective robot control for chemical cloaking demonstrated in simulations
- The neural network approach efficiently approximates PDE solutions
- Active cloaking outperforms passive metamaterial methods

## Abstract

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 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.