Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures

TL;DR

This paper introduces a probabilistic frontier prioritization method using Dirichlet process Gaussian mixtures to enhance multi-robot exploration efficiency, demonstrating consistent improvements in simulations and real-world tests.

Contribution

It presents a novel probabilistic approach to frontier prioritization that integrates DP-GMM into existing algorithms, improving exploration performance under various conditions.

Findings

Average gain of 10% and 14% in simulation performance.

Consistent improvement across environments with different clutter and communication constraints.

Successful real-world deployment with a dual-drone system.

Abstract

Multi-agent autonomous exploration is essential for applications such as environmental monitoring, search and rescue, and industrial-scale surveillance. However, effective coordination under communication constraints remains a significant challenge. Frontier exploration algorithms analyze the boundary between the known and unknown regions to determine the next-best view that maximizes exploratory gain. This article proposes an enhancement to existing frontier-based exploration algorithms by introducing a probabilistic approach to frontier prioritization. By leveraging Dirichlet process Gaussian mixture model (DP-GMM) and a probabilistic formulation of information gain, the method improves the quality of frontier prioritization. The proposed enhancement, integrated into two state-of-the-art multi-agent exploration algorithms, consistently improves performance across environments of varying clutter, communication constraints, and team sizes. Simulations showcase an average gain of $10\%$ and $14\%$ for the two algorithms across all combinations. Successful deployment in real-world experiments with a dual-drone system further corroborates these findings.