Deployment of an Aerial Multiagent System for Automated Task Execution in Large-Scale Underground Mining Environments

This is the authors' abstract. We don't add key points for this paper.

In this article, we present a framework for deploying aerial multi-agent systems in large-scale subterranean environments with minimal supporting infrastructure. The objective 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 execution 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.