On Endogenous Reconfiguration in Mobile Robotic Networks

TL;DR

This paper investigates reconfiguration strategies for mobile robotic networks with realistic motion constraints, proposing algorithms that outperform traditional methods and analyzing their coverage cost decay rates as the network size increases.

Contribution

It introduces novel algorithms for coverage problems in robotic networks with DI and DD models, achieving near-optimal performance and demonstrating the need for reconfiguration as networks grow.

Findings

Coverage cost decreases as m^{-1/3} for DI and DD models.

Proposed algorithms outperform conventional methods for large networks.

Reconfiguration is essential to maintain optimal coverage in expanding networks.

Abstract

In this paper, our focus is on certain applications for mobile robotic networks, where reconfiguration is driven by factors intrinsic to the network rather than changes in the external environment. In particular, we study a version of the coverage problem useful for surveillance applications, where the objective is to position the robots in order to minimize the average distance from a random point in a given environment to the closest robot. This problem has been well-studied for omni-directional robots and it is shown that optimal configuration for the network is a centroidal Voronoi configuration and that the coverage cost belongs to $\Theta(m^{-1/2})$, where $m$ is the number of robots in the network. In this paper, we study this problem for more realistic models of robots, namely the double integrator (DI) model and the differential drive (DD) model. We observe that the introduction of these motion constraints in the algorithm design problem gives rise to an interesting behavior. For a \emph{sparser} network, the optimal algorithm for these models of robots mimics that for omni-directional robots. We propose novel algorithms whose performances are within a constant factor of the optimal asymptotically (i.e., as $m \to +\infty$). In particular, we prove that the coverage cost for the DI and DD models of robots is of order $m^{-1/3}$. Additionally, we show that, as the network grows, these novel algorithms outperform the conventional algorithm; hence necessitating a reconfiguration in the network in order to maintain optimal quality of service.