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

2025-01-17

AI Summary

Large underground mines lack fixed infrastructure, hindering reliable multi-UAV coordination. Method: We propose an infrastructure-free autonomous aerial multi-robot system featuring (i) a reactive auction mechanism for dynamic task insertion and real-time allocation; (ii) behavior-tree synthesis via backward-chaining inference to enable on-demand task composition; and (iii) a full-stack solution integrating mobile Wi-Fi mesh networking with infrastructure-agnostic navigation, localization, and control. Results: In a real mine tunnel exceeding 200 meters, three UAVs successfully executed multi-point inspection, gas detection, and distributed sensing. Experiments demonstrate the system’s reliability, real-time performance, and operational viability under stringent underground constraints—specifically, in GPS-denied, infrastructure-free environments. This work establishes a scalable technical paradigm for multi-agent coordination in unstructured, unmapped subterranean settings.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Systems under Uncertainty
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Autonomous Drones
• Underground Mines
• Resource Allocation

Innovation

• Autonomous Drones
• Underground Mining Operations
• Auction-based Task Allocation