An arrovian analysis on the multi-robot task allocation problem: Analyzing a behavior-based architecture

Abstract

Research in multi-robot systems is a rich field that has attracted much attention in recent decades. However, robot coordination and task allocation to a correct mission accomplishment are still challenging even with technological advances. Despite many proposals presented in the literature, the applications and theories about the task allocation problem are not yet exhausted. Thus, this work proposes an axiomatic framework based on Social Choice Theory to analyze the task allocation problem in intentional cooperation multi-robot systems. It uses Kenneth J. Arrow’s framework of his famous Impossibility Theorem. The conditions imposed by Arrow aim to create an ideal for preference aggregation mechanisms through axiomatic analysis. This paper aims to transport this analysis to the multi-robot domain. A behavior-based Multi-robot Task Allocation architecture is used to present simulation results and discuss two cases in the ordinal preference domain. The analysis results show that using the proposed framework to analyze, under the Arrovian perspective, implemented MRTA architectures is feasible.