In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present an efficient centralized frontier sharing approach that maximizes exploration by taking into account information gain in the merged map, distance, and reward computation among frontier candidates and encourages the spread of agents into the environment. Eventually, our method efficiently spreads the robots for maximum exploration while keeping SLAM uncertainty low. Additionally, we also present two coordination approaches, synchronous and asynchronous to prioritize robot goal assignments by the central server. The proposed method is implemented in ROS and evaluated through simulation and experiments on publicly available datasets and similar methods, rendering promising results.

翻译：在自主机器人领域，一个关键挑战在于开发鲁棒的主动协作式SLAM解决方案，其中多台机器人需在智能协调自身运动与传感器数据采集的同时，协作探索并构建未知环境的地图。本文提出一种高效的中心化前沿共享方法，该方法通过综合考虑合并地图中的信息增益、前沿候选点之间的距离与奖励计算，最大化探索效率，并促使各智能体向环境扩散。最终，我们的方法在保持SLAM不确定性较低的同时，高效地分散机器人以实现最大程度的环境探索。此外，我们还提出了两种协调策略——同步异步与异步调度，用于优先处理中央服务器对机器人目标的分配。所提方法已在ROS系统中实现，并通过仿真实验与公开数据集上的对比实验验证，展现出显著优越的性能。