確率的フロンティア優先順位付けによるマルチロボット探索の強化：ディリクレ過程ガウス混合モデルを用いて
Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures
既存のフロンティア探索アルゴリズムに、ディリクレ過程ガウス混合モデルと確率的情報利得を導入し、フロンティアの優先順位付けを改善する手法を提案。シミュレーションで平均10〜14%の性能向上を確認し、実機のデュアルドローンでも有効性を実証した。
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著者: John Lewis Devassy, Meysam Basiri, Mário A. T. Figueiredo, Pedro U. Lima
分類: cs.RO
原文アブストラクト
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.
