The MPFA: A multiple-place foraging algorithm for biologically-inspired robot swarms

Abstract

Finding and retrieving resources in unmapped environments is an important and difficult challenge for robot swarms. Central-place foraging algorithms can be tuned to produce efficient collective strategies for different resource distributions. However, efficiency decreases as swarm size scales up: larger swarms produce more inter-robot collisions and increase competition for resources. We propose a novel extension to central-place foraging in which multiple nests are distributed in the environment. In this multiple-place foraging algorithm, robots depart from a home nest but always return to the nest closest to them. We simulate robot swarms that mimic foraging ants using the multiple-place strategy, employing a genetic algorithm to optimize their behavior in the robot simulator ARGoS. Experiments show that multiple nests produce higher foraging rates and lower average travel time compared to central-place foraging for three different resource distributions. Time spent avoiding robot-robot collisions is not always reduced as was expected, primarily because the use of pheromone-like waypoints leads to more collisions when robots forage for clustered resources. These results demonstrate the importance of careful design in order to create efficient multiple collection points to mitigate the central-place bottleneck for foraging robot swarms.