Deployment of an Aerial Multiagent System for Automated Task Execution in Large-Scale Underground Mining Environments
Niklas Dahlquist, Samuel Nordström, Nikolaos Stathoulopoulos, Björn Lindqvist, Akshit Saradagi, George Nikolakopoulos
AI summary
Problem
Underground mines are vast, dynamic, and perception-degraded, making it difficult to safely deploy autonomous multi-robot systems for routine inspections without heavy communication infrastructure.
Approach
A central auction-based allocator distributes tasks to drones on-the-fly, while each drone autonomously generates modular behavior trees from primitive capabilities to execute tasks independently.
Key results
- Field validation with three drones in a real underground mine
- Mobile Wi-Fi mesh enables reliable fleet communication
- Reactive auction system handles dynamic task additions and failures
- Automated behavior tree synthesis ensures modular local autonomy
Why it matters
Enables safer, more efficient routine inspections in hazardous underground mines by offloading dangerous tasks from humans to autonomous drone fleets.
Abstract
In this article, we present a framework for de- ploying aerial multi-agent systems in large-scale subterranean environments with minimal supporting infrastructure. The ob- jective is to optimally and reactively execute routine inspection tasks, selected by a mine operator on-the-fly. The assignment of currently available tasks to the agents is accomplished through an auction-based system, where the agents bid for available tasks, which are used by a central auctioneer to optimally assign the tasks. A mobile Wi-Fi mesh supports inter-agent communication and bi-directional communication between agents and the task allocator, while the task exe- cution is performed completely infrastructure-free. Given a task to be accomplished, 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 is reactive and supports the addition of new 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 as a proof-of-concept. The scalability, fault tolerance, and the influence of agent initializations on the multi-agent architecture have been tested through complementary Gazebo simulations in a cave environment. The proposed framework can be utilized in a subterranean environment for missions involving rapid inspection, gas detection, distributed sensing and mapping etc. The proposed framework and its field deployment contribute towards furthering reliable automation in large-scale subterranean environments to offload both routine and dangerous tasks from human operators to autonomous aerial robots.