用分阶段框架让机器人安全驱赶敌对目标，适应动态环境。
Dual-Stage Safe Herding Framework for Adversarial Attacker in Dynamic Environment
- 分两阶段设计：先构建虚拟围栏，再触发追捕机制
- 仿真中成功将敌对智能体引导至指定安全区且无碰撞
- 适合城市等复杂环境下的多机器人协同防御场景
近期机器人技术的发展使自主系统广泛部署于复杂操作环境，带来巨大机遇的同时也引发严峻安全问题。基于固定编队的传统驱赶方法在城市及障碍物密集场景中常失效或存在风险，尤其面对行为未知且可自适应的敌对智能体时。本文将此问题视为扩展的驱赶任务：防御型机器人需在动态环境中，安全引导具有未知策略的敌对智能体远离受保护区域并进入预定安全区，同时保持无碰撞导航。为此，提出一种基于可达-规避博弈理论与局部运动规划的分层混合框架，融合虚拟围栏机制与事件触发式追捕策略，实现可扩展且鲁棒的多智能体协同。仿真结果表明，该方法能有效实现敌对智能体的安全高效引导。
原文摘要 · Abstract (English)
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.
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