A Study of Scalability Properties in Robotic Teams

Summary

In this chapter we describe how the productivity of homogeneous robots scales with group size. Economists found that the addition of workers into a group results in their contributing progressively less productivity; a concept called the Law of Marginal Returns. We study groups that differ in their coordination algorithms, and note that they display increasing marginal returns only until a certain group size. After this point the groups’ productivity drops with the addition of robots. Interestingly, the group size where this phenomenon occurs varies between groups using differing coordination methods. We define a measure of interference that enables comparison, and find a high negative correlation between interference and productivity within these groups. Effective coordination algorithms maintain increasing productivity over larger groups by reducing the team’s interference levels. Using this result we are able to examine the productivity of robotic groups in several simulated domains in thousands of trials. We find that in theory groups should always add productivity during size scale-up, but spatial limitations within domains cause robots to fail to achieve this ideal. We believe that coordination methods can be developed that improve a group’s performance by minimizing interference. We present our findings of composite coordination methods that provide evidence of this claim.

This material is based upon work supported in part by the NSF under grant #0222914 and ISF grant #1211/04. Sarit Kraus is also affiliated with UMIACS.