Robotic fleets can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. However, it can be demanding or even impractical for an operator to directly control each robot. Thus, autonomy of the fleet and its online interaction with the operator are both essential, particularly in dynamic and partially unknown environments. The operator might need to add new tasks, cancel some tasks, change priorities and modify planning results. How to design the procedure for these interactions and efficient algorithms to fulfill these needs have been mostly neglected in the related literature. Thus, this work proposes a human-centric coordination and supervision scheme (HECTOR) for large-scale robotic fleets under continual and uncertain temporal tasks. It consists of three hierarchical layers: (I) the bidirectional and multimodal protocol of online human-fleet interaction, where the operator interacts with and supervises the whole fleet; (II) the rolling assignment of currently-known tasks to teams within a certain horizon, and (III) the dynamic coordination within a team given the detected subtasks during online execution. The overall mission can be as general as temporal logic formulas over collaborative actions. Such hierarchical structure allows human interaction and supervision at different granularities and triggering conditions, to both improve computational efficiency and reduce human effort. Extensive human-in-the-loop simulations are performed over heterogeneous fleets under various temporal tasks and environmental uncertainties.

翻译：机器人集群在并行协作执行任务时（如物流配送、安防监控、搜救行动）可展现出极高效率。然而，由操作员直接操控每台机器人既困难又不切实际。因此，集群的自主性及其与操作员的在线交互能力在动态且部分未知的环境中至关重要。操作员可能需添加新任务、取消某些任务、调整优先级并修改规划结果。如何设计满足这些需求的交互流程与高效算法，在相关文献中鲜有探讨。为此，本研究提出面向大规模机器人集群的人本中心分层协同与监督框架（HECTOR），适用于持续且不确定的时态任务。该框架包含三个层级：（I）双向多模态的在线人机交互协议，实现操作员对全集群的监督与交互；（II）在有限时间窗口内将已知任务滚动分配给子团队；（III）子团队基于在线执行中检测的子任务进行动态协调。整体任务可泛化为协同动作上的时态逻辑公式。这种分层架构支持操作员以不同粒度与触发条件进行交互监督，既提升计算效率又降低人力成本。针对异构集群在多种时态任务与环境不确定性下的场景，开展了大规模人在回路仿真实验。