Vertex addition to a ball graph with application to reliability and area coverage in autonomous swarms

TL;DR

This paper develops algorithms to optimize the placement of vertices in unit ball graphs to enhance reliability and area coverage in autonomous swarm networks, balancing robustness with spatial distribution.

Contribution

It extends previous algorithms to improve both reliability and area coverage in unit ball graphs for autonomous swarms, using efficient vertex repositioning methods.

Findings

Algorithm effectively increases graph reliability.

Method achieves balanced area coverage.

Comparison shows improved performance over existing algorithms.

Abstract

A unit ball graph consists of a set of vertices, labeled by points in Euclidean space, and edges joining all pairs of points within distance 1. These geometric graphs are used to model a variety of spatial networks, including communication networks between agents in an autonomous swarm. In such an application, vertices and/or edges of the graph may not be perfectly reliable; an agent may experience failure or a communication link rendered inoperable. With the goal of designing robust swarm formations, or unit ball graphs with high reliability (probability of connectedness), in a preliminary conference paper we provided an algorithm with cubic time complexity to determine all possible changes to a unit ball graph by repositioning a single vertex. Using this algorithm and Monte Carlo simulations, one obtains an efficient method to modify a unit ball graph by moving a single vertex to a location which maximizes the reliability. Another important consideration in many swarm missions is area coverage, yet highly reliable ball graphs often contain clusters of vertices. Here, we generalize our previous algorithm to improve area coverage as well as reliability. Our algorithm determines a location to add or move a vertex within a unit ball graph which maximizes the reliability, under the constraint that no other vertices of the graph be within some fixed distance. We compare this method of obtaining graphs with high reliability and evenly distributed area coverage to another method which uses a modified Fruchterman-Reingold algorithm for ball graphs.