Deployment of an Aerial Multi-agent System for Automated Task Execution in Large-scale Underground Mining Environments

TL;DR

This paper introduces a novel infrastructure-free aerial multi-agent system for automated inspection tasks in large underground mines, utilizing reactive auction-based task allocation and behavior trees, validated in real-world conditions.

Contribution

The paper presents a new decentralized framework for deploying aerial multi-agent systems in subterranean environments with minimal infrastructure support.

Findings

Successfully deployed in a real underground mine with three aerial agents.

Demonstrated effective reactive auction-based task allocation.

Enabled autonomous inspection and mapping in large-scale underground settings.

Abstract

In this article, we present a framework for deploying an aerial multi-agent system in large-scale subterranean environments with minimal infrastructure for supporting multi-agent operations. The multi-agent objective is to optimally and reactively allocate and execute inspection tasks in a mine, which are entered by a mine operator on-the-fly. The assignment of currently available tasks to the team of agents is accomplished through an auction-based system, where the agents bid for available tasks, which are used by a central auctioneer to optimally assigns tasks to agents. A mobile Wi-Fi mesh supports inter-agent communication and bi-directional communication between the agents and the task allocator, while the task execution is performed completely infrastructure-free. Given a task to be accomplished, a reliable and modular agent behavior is synthesized by generating behavior trees from a pool of agent capabilities, using a back-chaining approach. The auction system in the proposed framework is reactive and supports addition of new operator-specified tasks on-the-go, at any point through a user-friendly operator interface. The framework has been validated in a real underground mining environment using three aerial agents, with several inspection locations spread in an environment of almost 200 meters. The proposed framework can be utilized for missions involving rapid inspection, gas detection, distributed sensing and mapping etc. in a subterranean environment. The proposed framework and its field deployment contributes towards furthering reliable automation in large-scale subterranean environments to offload both routine and dangerous tasks from human operators to autonomous aerial robots.