Multi-Robot Exploration (MRE) systems with communication constraints have proven efficient in accomplishing a variety of tasks, including search-and-rescue, stealth, and military operations. While some works focus on opportunistic approaches for efficiency, others concentrate on pre-planned trajectories or scheduling for increased interpretability. However, scheduling usually requires knowledge of the environment beforehand, which prevents its deployment in several domains due to related uncertainties (e.g., underwater exploration). In our previous work, we proposed an intermittent communications framework for MRE under communication constraints that uses scheduled rendezvous events to mitigate such limitations. However, the system was unable to generate optimal plans and had no mechanisms to follow the plan considering realistic trajectories, which is not suited for real-world deployments. In this work, we further investigate the problem by formulating the Multi-Robot Exploration with Communication Constraints and Intermittent Connectivity (MRE-CCIC) problem. We propose a Mixed-Integer Linear Program (MILP) formulation to generate rendezvous plans and a policy to follow them based on the Rendezvous Tracking for Unknown Scenarios (RTUS) mechanism. The RTUS is a simple rule to allow robots to follow the assigned plan, considering unknown conditions. Finally, we evaluated our method in a large-scale environment configured in Gazebo simulations. The results suggest that our method can follow the plan promptly and accomplish the task efficiently. We provide an open-source implementation of both the MILP plan generator and the large-scale MRE-CCIC.

翻译：具有通信约束的多机器人探索系统已被证明在完成多种任务（如搜救、隐蔽行动和军事行动）方面具有高效性。部分研究侧重于提升效率的机会主义方法，另一些则关注于提高可解释性的预规划轨迹或调度方案。然而，调度通常需要预先掌握环境信息，这在存在相关不确定性（例如水下探索）的领域中限制了其应用。在我们先前的工作中，我们提出了一种通信约束下多机器人探索的间歇性通信框架，该框架利用预定会合事件来缓解此类限制。但该系统无法生成最优计划，且缺乏考虑实际轨迹的跟踪机制，难以适用于现实部署。本研究进一步探讨该问题，形式化定义了具有通信约束与间歇性连接的多机器人探索问题。我们提出了一种混合整数线性规划模型来生成会合计划，并基于未知场景会合跟踪机制设计了一种计划跟踪策略。该机制是一套简单的规则，使机器人能在未知条件下遵循既定计划。最后，我们在Gazebo仿真环境中构建的大规模场景中评估了所提方法。结果表明，该方法能够及时跟踪计划并高效完成任务。我们开源了混合整数线性规划计划生成器及大规模MRE-CCIC系统的实现代码。