Multi-Robot Unknown Area Exploration Using Frontier Trees

Abstract

This paper presents a novel approach for multi-robot unknown area exploration. Recently, the frontier tree data structure was used in single robot exploration to memorize frontiers, their positions, exploration state, and the map. This tree could be queried to decide on further exploration steps. In this paper, we take the concept further for multi-robot exploration by proposing a new abstraction called the ‘group,’ meant to share information through a common frontier tree, requisite operations at the group level, and a method to assign goals to multiple robots. A group is a set of robots, the union of whose explored regions forms a contiguous region (a single connected region in a topological sense). As a group has precisely one tree, the robots share a common state of the exploration task. We propose techniques to merge groups and their frontier trees once their maps overlap. Finally, we suggest a method to designate and assign exploration goals to the individual robots by choosing nodes from the frontier tree. The proposed approach outperforms seven state-of-the-art research works in simulation.