The Importance of Information Flow Regulation in Preferentially Foraging Robot Swarms

Abstract

Instead of committing to the first source of reward that it discovers, an agent engaged in "preferential foraging" continues to choose between different reward sources in order to maximise its foraging efficiency. In this paper, the effect of preferential source selection on the performance of robot swarms with different recruitment strategies is studied. The swarms are tasked with foraging from multiple sources in dynamic environments where worksite locations change periodically and thus need to be re-discovered. Analysis indicates that preferential foraging leads to a more even exploitation of resources and a more efficient exploration of the environment provided that information flow among robots, that results from recruitment, is regulated. On the other hand, preferential selection acts as a strong positive feedback mechanism for favouring the most popular reward source when robots exchange information rapidly in a small designated area, preventing the swarm from foraging efficiently and from responding to changes.

Swarm Foraging Review: Closing the Gap Between Proof and Practice

“…Recently, Pitonakova et al [28][29][30] proposed a framework that contextualizes many aspects of foraging in terms of information. Robot foragers succeeded or failed based on the amount and quality of information gathered during foraging and their ability to act on that information.…”