Exploiting Clusters For Complete Resource Collection In Biologically-Inspired Robot Swarms

The complete collection of resources from a pre-defined search area is a challenging task for autonomous robot swarms. Because naturally-occurring resources are likely to be distributed in clusters, foraging robot swarms can identify and exploit these resource clusters to improve collection efficiency. We describe an ant-inspired robot swarm foraging system that searches for and collects resources from a variety of distributions, and a cluster prediction and exploitation algorithm that augments swarm foraging by directing robots to residual resources. By characterizing the cumulative resource collection time for a robot swarm foraging in a variety of clustered resource distributions, we can identify the relationship between the "clusteredness" of the distribution and the change in the resource collection rate over time. Experiments show that collection efficiency is most significantly increased when robots switch from ant-inspired foraging to focused exploitation of clusters after approximately 90% of resources have been collected. Not surprisingly, clustering algorithms are most effective when resources are highly clustered in the environment. This work demonstrates the feasibility of efficient, complete resource collection using simple, range-limited robot swarms programmed with ant-inspired foraging behaviors.