# Distributed Adaptive Coverage Control of Differential Drive Robotic Sensors

## TL;DR

This paper develops adaptive control algorithms for multiple differential drive robots to autonomously cover a region with unknown density, using L2-distance and consensus methods, validated through real robot experiments.

## Contribution

It introduces novel adaptive control laws for coverage with unknown density functions and incorporates consensus for improved parameter convergence.

## Key findings

- Algorithms successfully deployed on real robots.
- Adaptive control improves coverage accuracy.
- Comparison shows L2-distance method's effectiveness.

## Abstract

This paper is concerned with the deployment of multiple mobile robots in order to autonomously cover a region Q. The region to be covered is described using a density function which may not be apriori known. In this paper, we pose the coverage problem as an optimization problem over some space of functions on Q. In particular, we look at L 2 -distance based coverage algorithm and derive adaptive control laws for the same. We also propose a modified adaptive control law incorporating consensus for better parameter convergence. We implement the algorithms on real differential drive robots with both simulated density function as well as density function implemented using light sources. We also compare the L 2 -distance based method with the locational optimization method using experiments.