Navigating teams of unmanned vehicles through environments containing dynamic, adversarial Weapon Engagement Zones~(WEZs) poses a fundamental challenge to mission success: a single vehicle, however capable its onboard guidance, remains a single point of failure. This paper presents a role-differentiated multi-agent framework for collaborative threat-aware trajectory planning in which a fleet of Autonomous Collaborative Platforms~(ACPs) is assigned distinct roles primary intercept, escort, and decoy to improve team-level mission success probability while managing individual WEZ exposure. Each ACP independently employs a reactive guidance law derived from the Collision Sphere Boundary for Evader Zero-Set~(CSBEZ), which accounts for pursuer maneuverability constraints imposed by minimum turn radius, and steers the vehicle toward the safest heading that also makes progress toward its goal. Role assignment and spatial route separation induce two complementary effects: probabilistic redundancy, in which $N$ independent paths raise the team success probability and threat saturation, in which lower-priority escorts and decoys draw adversary attention and free the primary vehicle to transit uncontested.

翻译：在包含动态对抗性武器交战区（WEZ）的环境中引导无人车团队航行，对任务成功构成根本性挑战：单一车辆，即使其机载制导能力再强，仍是单点故障点。本文提出一种角色差异化多智能体框架，用于协同威胁感知轨迹规划。在该框架中，自主协同平台（ACP）车队被分配不同角色——主拦截、护航与诱饵——以在管理个体WEZ暴露的同时，提升团队级别的任务成功概率。每个ACP独立采用基于碰撞球边界逃逸零点集（CSBEZ）导出的反应式制导律，该制导律考虑由最小转弯半径施加的追击器机动能力约束，并将车辆导向既能朝向目标前进又最安全的航向。角色分配与空间路径分离产生两种互补效应：概率冗余（其中$N$条独立路径提升团队成功概率）与威胁饱和（其中优先级较低的护航与诱饵平台吸引敌方注意力，使主平台通行无阻）。