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：在自主机器人学中，一个关键挑战在于为主动协作SLAM开发鲁棒的解决方案，其中多个机器人协作探索并映射未知环境，同时智能协调其运动与传感器数据采集。本文提出一种高效的集中式前沿共享方法，该方法通过综合考虑融合地图中的信息增益、前沿候选点之间的距离与奖励计算来最大化探索效率，并促进智能体在环境中的分散分布。最终，我们的方法在保持SLAM不确定性较低的同时，有效分散机器人以实现最大化探索。此外，我们还提出了两种协调策略——同步与异步方式，通过中央服务器对机器人目标分配进行优先级排序。所提方法在ROS中实现，并通过公开数据集与同类方法的仿真与实验进行评估，取得了令人满意的结果。