  Individual robots are not effective at exploring large unmapped areas. An
alternate approach is to use a swarm of simple robots that work together,
rather than a single highly capable robot. The central-place foraging algorithm
(CPFA) is effective for coordinating robot swarm search and collection tasks.
Robots start at a centrally placed location (nest), explore potential targets
in the area without global localization or central control, and return the
targets to the nest. The scalability of the CPFA is limited because large
numbers of robots produce more inter-robot collisions and large search areas
result in substantial travel costs. We address these problems with the
multiple-place foraging algorithm (MPFA), which uses multiple nests distributed
throughout the search area. Robots start from a randomly assigned home nest but
return to the closest nest with found targets. We simulate the foraging
behavior of robot swarms in the robot simulator ARGoS and employ a genetic
algorithm to discover different optimized foraging strategies as swarm sizes
and the number of targets are scaled up. In our experiments, the MPFA always
produces higher foraging rates, fewer collisions, and lower travel and search
time compared to the CPFA for the partially clustered targets distribution. The
main contribution of this paper is that we systematically quantify the
advantages of the MPFA (reduced travel time and collisions), the potential
disadvantages (less communication among robots), and the ability of a genetic
algorithm to tune MPFA parameters to mitigate search inefficiency due to less
communication.
