関与度を考慮したLLMエージェントによる追跡回避

原文アブストラクト

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.