Efficient quantitative assessment of robot swarms: coverage and targeting Lévy strategies
This provides incremental improvements for researchers in swarm robotics by offering faster quantitative assessment tools.
The paper tackled the problem of efficiently assessing robot swarm performance by applying continuum modeling tools from biological systems to compute metrics like coverage and hitting times, resulting in computationally fast methods that confirm and quantify the advantage of Lévy strategies over Brownian motion for search and area coverage.
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 Lévy 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 Lévy strategies over Brownian motion for search and area coverage problems in swarm robotics.