Plan-and-Avoid: Real-Time Aircraft Trajectory Coordination in a Multi-Agent Environment

Advanced Air Mobility (AAM) operations are expected to significantly increase aerial traffic in urban airspace, requiring autonomous traffic management systems to ensure collision-free operations in highly congested environments. In this paper, we propose a multi-agent coordination framework that uses minimum time-to-reach (TTR) as a unifying metric for priority assignment, temporal separation, and safety filtering. We focus on the problem of coordinating multiple aerial vehicles merging into an air corridor while maintaining safe separation between vehicles. Vehicles are assigned arrival-consistent priority based on TTR, and target TTR values are used to enforce temporal spacing that induces spatial separation. A priority-consistent safety filtering layer based on Hamilton-Jacobi reachability value functions ensures collision avoidance while minimally modifying the reference guidance. Simulation results in a highly congested corridor merging scenario show that the proposed method improves safety, fairness, and efficiency compared to time-optimal guidance and priority-agnostic safety filtering.

Current spacecraft collision-avoidance operations rely on intermittent ground-station contacts, requiring operators to plan with delayed and asynchronously updated information. Consequently, maneuvers must be planned with only intermittent information sharing between operators, raising the question of how much coordination is needed to achieve collision-avoidance performance comparable to centralized planning. Although decision-theoretic approaches such as partially observable Markov decision processes (POMDPs) capture the sequential and uncertain nature of collision avoidance, existing multiagent extensions typically assume either continuous information sharing or communication models that do not reflect operational ground-station constraints. To explicitly model this intermittent information availability, we formulate the spacecraft-to-spacecraft collision avoidance problem as a semi-decentralized POMDP (SDec-POMDP), where we govern information propagation directly by realistic ground-station visibility windows. Joint maneuver policies are computed using approximate Recursive Small-Step Semi-Decentralized A* (RS-SDA*), following the state-of-the-art A*-based lineage for decentralized multiagent planning. Across a representative suite of conjunction scenarios, semi-decentralized planning recovers near-centralized maneuver quality while requiring 28.5% fewer synchronization events than continuous coordination. Comparisons with representative rule-based operator heuristics further show that communication-aware planning more consistently achieves the desired operational miss-distance band while minimizing unnecessary trajectory deviation. Together, these results establish a practical planning framework for autonomous collision avoidance under realistic intermittent communication, bridging the gap between idealized centralized coordination and fully decentralized planning execution.

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.