We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots.

翻译：我们提出了一种面向分布式多机器人团队的自主探索算法，该算法考虑了距离传感移动机器人的地图与定位不确定性。通过引入虚拟地标，量化过程噪声与传感器噪声对地图不确定性的综合影响。此外，我们采用基于迭代期望最大化的算法，评估本地机器人及其邻居下一步动作的潜在结果。为验证框架的有效性，我们与当前最先进的算法进行了对比分析。实验结果表明，所提算法能够在抑制地图不确定性和实现机器人间高效任务分配之间取得平衡。