A Case Study in Recovery of Drones using Discrete-Event Systems

2026-04-23

AI Summary

This study addresses the challenge of safely recovering and re-admitting unmanned aerial vehicle (UAV) swarms into controlled airspace following faults or adversarial attacks. The authors propose a hybrid architecture that integrates a high-level discrete-event system supervisor with low-level continuous controllers. For the first time, discrete-event supervisory control theory is applied to swarm recovery tasks, featuring a two-tier recovery supervisor that coordinates topological reconfiguration and reformation of disconnected UAVs. The approach is evaluated on the PyBullet-Drones simulation platform under four distinct initial state estimation scenarios, demonstrating successful and safe recovery and re-entry of a ten-UAV swarm. The results validate the method’s effectiveness and robustness in complex, uncertain environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: State Estimation
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Discrete-event systems and supervisory control theory provide a rigorous framework for specifying correct-by-construction behavior. However, their practical application to swarm robotics remains largely underexplored. In this paper, we investigate a topological recovery method based on discrete-event-systems within a swarm robotics context. We propose a hybrid architecture that combines a high-level discrete event systems supervisor with a low-level continuous controller, allowing lost drones to safely recover from fault or attack events and re-enter a controlled region. The method is demonstrated using ten simulated UAVs in the py-bullet-drones framework. We show recovery performance across four distinct scenarios, each with varying initial state estimates. Additionally, we introduce a secondary recovery supervisor that manages the regrouping process for a drone after it has re-entered the operational region.

Problem

• drone recovery
• swarm robotics
• discrete-event systems
• fault recovery
• UAV

Innovation

• Discrete-Event Systems
• Supervisory Control Theory
• Swarm Robotics
• UAV Recovery
• Hybrid Architecture