Collaborative Threat-Aware Autonomy (CTAA)

2026-05-25

AI Summary

This work addresses the vulnerability of unmanned vehicle swarms to single-point failures in dynamic, adversarial weapon engagement zones (WEZs) by proposing a role-differentiated multi-agent cooperative framework. The approach dynamically assigns platforms to distinct roles—primary interceptor, escort, and decoy—and integrates a reactive guidance law based on the Collision Sphere Boundary Escape Zero-set (CSBEZ) with a maneuverability model constrained by minimum turning radius. This enables collaborative threat awareness and real-time trajectory optimization. Through role specialization and path separation, the method simultaneously achieves probabilistic redundancy and threat saturation effects, significantly enhancing swarm survivability and mission success rates while effectively reducing threat exposure for critical assets.

Abstract

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.

Problem

• Collaborative Threat-Aware Autonomy
• Weapon Engagement Zones
• multi-agent trajectory planning
• mission success probability
• single point of failure

Innovation

• Collaborative Threat-Aware Autonomy
• role-differentiated multi-agent
• Weapon Engagement Zones
• CSBEZ guidance law
• probabilistic redundancy