Lévy walk enhances efficiency of group foraging in pheromone-communicating swarm robots

Abstract

This paper describes an implementation of Lévy walk (or Lévy flight) to pheromone communicating swarm robots. Lévy flight is a special class of random walk in which the step length distribution is given by power law distribution. Lévy flight is known to maximize the efficiency of resource searches in uncertain environments. Using computer simulations, we show that the Lévy walk-like searching strategy can maximize the group foraging efficiency of the swarm robots using pheromone trails (mimicking ant group foraging), as well as maximize individual searching area. The Lévy walk was achieved by adjusting the probability per unit time with which an individual robot moves forward (otherwise it turns to right, to left, and reverse). We discuss the effect of swarming on optimal parameter values of Lévy walk. Optimization of individual searching strategies should be studied further, both in swarm robots and real organisms.