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Biologically inspired strategies have long been adapted to swarm roboticsystems, including biased random walks, reaction to chemotactic cues andlong-range coordination. In this paper we apply analysis tools developed formodeling biological systems, such as continuum descriptions, to the efficientquantitative characterization of robot swarms. As an illustration, bothBrownian and L\'{e}vy strategies with a characteristic long-range movement arediscussed. As a result we obtain computationally fast methods for theoptimization of robot movement laws to achieve a prescribed collectivebehavior. We show how to compute performance metrics like coverage and hittingtimes, and illustrate the accuracy and efficiency of our approach for areacoverage and search problems. Comparisons between the continuum model androbotic simulations confirm the quantitative agreement and speed up of ourapproach. Results confirm and quantify the advantage of L\'{e}vy strategiesover Brownian motion for search and area coverage problems in swarm robotics.

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