In large unknown environments, search operations can be much more time-efficient with the use of multi-robot fleets by parallelizing efforts. This means robots must efficiently perform collaborative mapping (exploration) while simultaneously searching an area for victims (coverage). Previous simultaneous mapping and planning techniques treat these problems as separate and do not take advantage of the possibility for a unified approach. We propose a novel exploration-coverage planner which bridges the mapping and search domains by growing sets of random trees rooted upon a pose graph produced through mapping to generate points of interest, or tasks. Furthermore, it is important for the robots to first prioritize high information tasks to locate the greatest number of victims in minimum time by balancing coverage and exploration, which current methods do not address. Towards this goal, we also present a new multi-robot task allocator that formulates a notion of a hierarchical information heuristic for time-critical collaborative search. Our results show that our algorithm produces 20% more coverage efficiency, defined as average covered area per second, compared to the existing state-of-the-art. Our algorithms and the rest of our multi-robot search stack is based in ROS and made open source

翻译：在大型未知环境中，通过并行化多机器人编队可显著提升搜索作业的时间效率。这意味着机器人需在执行协同建图（探索）的同时高效搜寻受害区域（覆盖）。现有同步建图与规划方法将这两个问题独立处理，未能利用统一方法的潜力。我们提出一种新颖的探索-覆盖规划器，通过在建图生成的位姿图上生长随机树集合来生成兴趣点（即任务），从而桥接建图与搜索领域。此外，机器人需优先处理高信息量任务，通过平衡覆盖与探索在最短时间内定位最多受害者——当前方法尚未解决此问题。为此，我们提出一种新型多机器人任务分配器，该分配器构建了层次化信息启发式概念用于时间关键型协同搜索。结果表明：与现有最优方法相比，我们算法在覆盖率效率（定义为每秒平均覆盖面积）上提升20%。本算法及多机器人搜索栈均基于ROS实现并以开源形式发布。