Dual-Stage Safe Herding Framework for Adversarial Attacker in Dynamic Environment

2025-09-10

AI Summary

This work addresses the challenge of safely herding adversarial agents with unknown, adaptive behaviors in dynamic environments using multi-robot systems, while ensuring zero-collision operation and protection of a designated safe zone. Method: We propose a two-stage safe herding framework integrating reach-avoid differential games with event-triggered pursuit mechanisms. A virtual containment boundary is introduced, and a hierarchical hybrid architecture unifies game-theoretic strategy generation, local motion planning, and multi-agent coordination control. Contribution/Results: Compared to prior approaches, the framework significantly enhances robustness, scalability, and real-time responsiveness in unstructured, dynamic scenarios. Extensive simulations demonstrate that the system reliably and efficiently expels adversarial agents from the protected region and guides them to a designated safe area—achieving stable, collision-free herding under complex environmental dynamics.

Technology Category

• Multiagent Systems: Adversarial Agents
• Intelligent Robots: Multi-Robot Systems
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Recent advances in robotics have enabled the widespread deployment of autonomous robotic systems in complex operational environments, presenting both unprecedented opportunities and significant security problems. Traditional shepherding approaches based on fixed formations are often ineffective or risky in urban and obstacle-rich scenarios, especially when facing adversarial agents with unknown and adaptive behaviors. This paper addresses this challenge as an extended herding problem, where defensive robotic systems must safely guide adversarial agents with unknown strategies away from protected areas and into predetermined safe regions, while maintaining collision-free navigation in dynamic environments. We propose a hierarchical hybrid framework based on reach-avoid game theory and local motion planning, incorporating a virtual containment boundary and event-triggered pursuit mechanisms to enable scalable and robust multi-agent coordination. Simulation results demonstrate that the proposed approach achieves safe and efficient guidance of adversarial agents to designated regions.

Problem

• Safely guiding adversarial agents away from protected areas
• Handling unknown strategies in dynamic obstacle-rich environments
• Maintaining collision-free navigation during multi-agent herding operations

Innovation

• Hierarchical hybrid framework with reach-avoid game theory
• Virtual containment boundary and event-triggered pursuit mechanisms
• Safe multi-agent coordination in dynamic environments