coExplore: Combining multiple rankings for multi-robot exploration

TL;DR

coExplore introduces a novel multi-robot exploration algorithm that enhances frontier selection by incorporating information gain, leading to faster exploration in urban environments while maintaining performance in open areas.

Contribution

The paper presents a new multi-robot exploration method that combines multiple rankings, including information gain, to improve frontier selection and exploration efficiency.

Findings

Outperforms state-of-the-art by 5% in simulation

Improves exploration time in urban environments

Maintains similar performance in open environments

Abstract

Multi-robot exploration is a field which tackles the challenge of exploring a previously unknown environment with a number of robots. This is especially relevant for search and rescue operations where time is essential. Current state of the art approaches are able to explore a given environment with a large number of robots by assigning them to frontiers. However, this assignment generally favors large frontiers and hence omits potentially valuable medium-sized frontiers. In this paper we showcase a novel multi-robot exploration algorithm, which improves and adapts the existing approaches. Through the addition of information gain based ranking we improve the exploration time for closed urban environments while maintaining similar exploration performance compared to the state-of-the-art for open environments. Accompanying this paper, we further publish our research code in order to lower the barrier to entry for further multi-robot exploration research. We evaluate the performance in three simulated scenarios, two urban and one open scenario, where our algorithm outperforms the state of the art by 5% overall.