arXiv:2501.10262cs.ROcs.SY2025-01被引 5

用无人机群自动执行地下矿场巡检,无需基础设施支持。

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

  • 基于竞拍机制分配任务,无人机自主竞价并执行
  • 在近200米范围的地下矿场完成多机协同巡检验证
  • 适合快速巡检、气体检测等危险场景,降低人力风险

本文提出一种在大型地下环境中部署空中多智能体系统的框架,实现无基础设施支持下的多智能体协同作业。矿工通过界面实时输入巡检任务,系统采用基于竞拍的任务分配机制,由中央拍卖器根据无人机竞价结果最优分配任务。通信依赖移动式Wi-Fi自组网,支持智能体间及与任务调度器的双向通信,任务执行全程无需固定设施。针对具体任务,通过反向链式方法从智能体能力池生成模块化行为树以合成可靠行为。该框架支持任务动态添加,具备良好的实时响应性。在真实地下矿场环境中,使用三架空中机器人,在约200米范围内的多个巡检点完成验证。系统可应用于快速巡检、气体探测、分布式感知与建图等任务。该框架及其实地部署推动了大型地下环境中的可靠自动化,将重复性与高危任务交由自主飞行机器人完成。

原文摘要 · Abstract (English)

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.

多智能体无人机地下巡检自动化

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