iHERO: Interactive Human-oriented Exploration and Supervision Under Scarce Communication

TL;DR

iHERO is an interactive framework enabling human operators to effectively supervise and coordinate autonomous robot fleets during exploration tasks with limited communication, ensuring timely updates and dynamic control.

Contribution

This work introduces a novel online coordination framework that integrates human supervision into multi-robot exploration under communication constraints, optimizing exploration and operator interaction.

Findings

Enhanced exploration efficiency demonstrated in simulations and hardware tests.

Effective human-robot interaction maintained with low latency.

Framework adaptable to real-world exploration scenarios.

Abstract

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/.