On determination of goal candidates in frontier-based multi-robot exploration

Abstract

Frontier-based approach can be considered as a de facto standard method for a mobile robot exploration task. Many variants have been proposed; however, relatively little attention has been made to study the influence of goal candidates generation to the performance of the exploration. In regular approaches, frontiers are considered as eventual goals for the next-best-view selection using a utility function combining a distance cost and expected information gain. The aim of this paper is to show that using goal candidates that are independent of the distance cost can improve the performance of exploration strategies. The found insights are supported by a statistical evaluation of thousands of trials performed for various environments.