Spatial Interference Reduction for Multi–Robot Systems Using Rational And Team–Based Aggression

Abstract

A team of robots with no centralized control performing a transportation task in a confined environment frequently interfere with each other. Previous work has shown that stereotyped robot--robot competition, inspired by aggressive displays in animals, can be used to effectively reduce such interference and improve overall system performance. Two principled approaches to determining aggression for robots are described in this thesis. The first, global investment, is based on the concept of 'economical investment'. The second, team-based aggression, extends and improves upon the economical investment scheme by increasing the coordination between robots. Simulation results show that both approaches improve the system efficiency compared to the approach of setting robots' aggression at random, and team--based aggression provides the best performance yet observed. The thesis also introduces a new scheme for implementing aggression functions using a simple network model. Further, the effects of interference-reduction methods over a range of population sizes are studied.