  Exploration of unknown scenes before human entry is essential for safety and
efficiency in numerous scenarios, e.g., subterranean exploration,
reconnaissance, search and rescue missions. Fleets of autonomous robots are
particularly suitable for this task, via concurrent exploration, multi-sensory
perception and autonomous navigation. Communication however among the robots
can be severely restricted to only close-range exchange via ad-hoc networks.
Although some recent works have addressed the problem of collaborative
exploration under restricted communication, the crucial role of the human
operator has been mostly neglected. Indeed, the operator may: (i) require
timely update regarding the exploration progress and fleet status; (ii)
prioritize certain regions; and (iii) dynamically move within the explored
area; To facilitate these requests, this work proposes an interactive
human-oriented online coordination framework for collaborative exploration and
supervision under scarce communication (iHERO). The robots switch smoothly and
optimally among fast exploration, intermittent exchange of map and sensory
data, and return to the operator for status update. It is ensured that these
requests are fulfilled online interactively with a pre-specified latency.
Extensive large-scale human-in-the-loop simulations and hardware experiments
are performed over numerous challenging scenes, which signify its performance
such as explored area and efficiency, and validate its potential applicability
to real-world scenarios. The videos are available on
https://zl-tian.github.io/iHERO/.
