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

Source: [Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures](<https://www.sota2.com/research/paper/69d33def7d0172140ec735fc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-06T05:00:11.965Z

Authors: John Lewis Devassy, Meysam Basiri, M\\'ario A. T. Figueiredo, Pedro U. Lima

Published: 2026-04-03T13:51:23.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Robot Exploration on Sparse Forest 0.1 TREES / m2 (test)](<https://www.sota2.com/research/sota/robot-exploration-on-sparse-forest-0-1-trees-m2-test.md>): Robot Exploration; Sparse Forest 0.1 TREES / m2 (test); Exploration Time: 50.64.

- [Robot Exploration on Mid-density Forest 0.15 TREES / m2 (test)](<https://www.sota2.com/research/sota/robot-exploration-on-mid-density-forest-0-15-trees-m2-test.md>): Robot Exploration; Mid-density Forest 0.15 TREES / m2 (test); Exploration Time: 53.2.

- [Robot Exploration on Dense Forest 0.2 TREES / m2 (test)](<https://www.sota2.com/research/sota/robot-exploration-on-dense-forest-0-2-trees-m2-test.md>): Robot Exploration; Dense Forest 0.2 TREES / m2 (test); Exploration Time: 79.

- [Robot Exploration on Multi-density Forest Mixed (test)](<https://www.sota2.com/research/sota/robot-exploration-on-multi-density-forest-mixed-test.md>): Robot Exploration; Multi-density Forest Mixed (test); Exploration Time: 87.73.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.03042>)

[PDF](<https://arxiv.org/pdf/2604.03042.pdf>)