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.

翻译：多智能体自主探索对于环境监测、搜救以及工业规模监控等应用至关重要。然而，在通信约束下实现有效协调仍然是一项重大挑战。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最佳视角。本文提出对现有基于前沿的探索算法进行改进，引入了一种前沿优先级的概率方法。通过利用狄利克雷过程高斯混合模型（DP-GMM）和概率化的信息增益公式，该方法提升了前沿优先级排序的质量。将所提出的增强方案集成到两种最先进的多智能体探索算法中，该方案在各种杂乱程度、通信约束和团队规模的环境下均能持续提升性能。仿真结果表明，两种算法在所有组合下的平均增益分别为$10\%$和$14\%$。在双无人机系统实际实验中的成功部署进一步证实了这些发现。