捕获、防护或中和：基于交战感知的追逃
Engagement-Aware Agentic Pursuit-Evasion
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中文总结 AI 辅助
研究多智能体对抗环境下的追逃问题，提出分层控制架构，将战略规划与安全保证解耦。通过零和滚动时域博弈及MPC求解，利用横向速度惩罚和CBF保证安全。模拟实验表明该框架能灵活切换策略，不同规则下性能稳健且控制逻辑不变。
中文摘要 AI 辅助
本文介绍了一种用于多智能体对抗环境的分层控制架构，将战略任务规划与严格的安全保证解耦。该系统将追逃问题表述为零和滚动时域博弈，通过迭代极小极大模型预测控制（MPC）方案求解。这使追捕者能够利用横向速度惩罚来预测和阻挡逃避者的轨迹，而非依赖反应式启发式编队。为确保无碰撞运行且不影响MPC的凸性，离散时间控制障碍函数（CBF）作为内环安全滤波器运行。通过模拟实验展示了框架的适应性，通过简单改变共享零和收益和CBF约束的权重，群体可灵活从激进的追逃策略切换到严格的周边防御和区域封锁，在不同交战规则下展现出强大性能且控制逻辑无需结构改变。源代码可通过此https URL获取。
英文摘要
This paper presents a hierarchical multi-agent architecture in which independent large language model (LLM) planners perform strategic role assignment for attacking and defending robot teams, decoupled from low-level control execution. At each planning cycle, each team's LLM planner observes its own team in full but the opposing team only within its robots' combined field of view, then assigns each robot a tactical role - e.g., hold a perimeter, neutralize an intruder on contact, or converge with teammates for capture - together with a natural-language justification. Each robot independently executes its assigned role through a receding-horizon model predictive control (MPC) controller, followed by a discrete-time control barrier function (CBF) filter for safety and role-dependent engagement constraints. Differentiated capture and neutralization incentives require the defending planner to balance threat resolution against resource allocation under partial observability. We evaluate the framework across variable-sized adversary teams using both state-based tactical reasoning and image-based contact classification. Results show that collective team behavior can be adapted through high-level LLM role assignment while retaining the same underlying low-level control architecture.
