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 two approaches for coordinating a system consisting of multiple robots to perform Active Collaborative SLAM (AC-SLAM) for environmental exploration. Our two coordination approaches, synchronous and asynchronous implement a methodology to prioritize robot goal assignments by the central server. We also present a method to efficiently spread the robots for maximum exploration while keeping SLAM uncertainty low. Both coordination approaches were evaluated through simulation and experiments on publicly available datasets, 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多机器人主动协同SLAM解决方案，即多个机器人智能协调运动与传感器数据采集，共同探索并构建未知环境的地图。本文提出了两种方法用于协调多机器人系统以执行环境探索中的主动协同SLAM（AC-SLAM）。这两种协调方法——同步法与异步法——均实现了一种由中央服务器优先分配机器人目标点的策略。同时，我们提出了一种高效分散机器人的方法，在保持SLAM不确定性较低的前提下最大化探索范围。通过在公开数据集上的仿真与实验验证，两种协调方法均呈现了令人满意的结果。