用概率方法提升多机器人探索效率，更好应对通信限制。
Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures
- 用狄利克雷过程高斯混合模型分析未知区域边界
- 在不同环境下平均提升探索效率10%至14%
- 适合需要协同探索的无人机、搜救等场景
多机器人自主探索在环境监测、搜救和工业级监视中至关重要，但通信受限下的有效协同仍是挑战。前沿探索算法通过分析已知与未知区域的边界，确定能最大化探索收益的下一视角。本文提出一种基于概率的前沿优先级优化方法，结合狄利克雷过程高斯混合模型（DP-GMM）与信息增益的概率公式，提升前沿选择质量。该方法被集成到两种先进多机器人探索算法中，在不同复杂度环境、通信约束及团队规模下均表现更优。仿真结果表明，两类算法在所有组合下平均探索效率分别提升10%和14%。双无人机系统的真实实验也验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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