Rendezvous aims at gathering all robots at a specific location, which is an important collaborative behavior for multirobot systems. However, in an unknown environment, it is challenging to achieve rendezvous. Previous researches mainly focus on special scenarios where communication is not allowed and each robot executes a random searching strategy, which is highly time-consuming, especially in large-scale environments. In this work, we focus on rendezvous in unknown environments where communication is available. We divide this task into two steps: rendezvous based environment exploration with relative pose (RP) estimation and rendezvous point election. A new strategy called partitioned and incomplete exploration for rendezvous (PIER) is proposed to efficiently explore the unknown environment, where lightweight topological maps are constructed and shared among robots for RP estimation with very few communications. Then, a rendezvous point selection algorithm based on the merged topological map is proposed for efficient rendezvous for multi-robot systems. The effectiveness of the proposed methods is validated in both simulations and real-world experiments.

翻译：集结旨在将所有机器人聚集于特定位置，是多机器人系统的重要协作行为。然而在未知环境下实现集结具有挑战性。现有研究主要关注无通信的特殊场景，各机器人执行随机搜索策略，此类方法在大规模环境中尤其耗时。本文聚焦于具备通信能力的未知环境集结问题，将任务分解为两步：基于相对位姿估计的集结式环境探索与集结地点选举。提出一种名为"分区非完全探索集结"的新策略，通过构建轻量拓扑地图并通过极少的通信量在机器人间共享以实现相对位姿估计，从而高效探索未知环境。随后，基于融合拓扑地图提出集结地点选择算法，实现多机器人系统的高效集结。通过仿真与真实实验验证了所提方法的有效性。