Efficient quantitative assessment of robot swarms: coverage and targeting L\'{e}vy strategies

TL;DR

This paper introduces a continuum modeling approach to efficiently evaluate and optimize robot swarm strategies, demonstrating that Lévy flights outperform Brownian motion in coverage and search tasks.

Contribution

It applies biological modeling tools to swarm robotics, providing fast methods to analyze and optimize movement strategies like Lévy flights for collective tasks.

Findings

Lévy strategies outperform Brownian motion in coverage and search tasks

The continuum model accurately predicts robotic swarm behavior

The approach significantly speeds up the evaluation process

Abstract

Biologically inspired strategies have long been adapted to swarm robotic systems, including biased random walks, reaction to chemotactic cues and long-range coordination. In this paper we apply analysis tools developed for modeling biological systems, such as continuum descriptions, to the efficient quantitative characterization of robot swarms. As an illustration, both Brownian and L\'{e}vy strategies with a characteristic long-range movement are discussed. As a result we obtain computationally fast methods for the optimization of robot movement laws to achieve a prescribed collective behavior. We show how to compute performance metrics like coverage and hitting times, and illustrate the accuracy and efficiency of our approach for area coverage and search problems. Comparisons between the continuum model and robotic simulations confirm the quantitative agreement and speed up of our approach. Results confirm and quantify the advantage of L\'{e}vy strategies over Brownian motion for search and area coverage problems in swarm robotics.