Technology Category
Game Theory and Economic Paradigms: Coordination and CollaborationMultiagent Systems: Coordination and CollaborationHumans and AI: Planning and Decision Support for Human-Machine Teams
Game-Theoretic Coordination For Time-Critical Missions of UAV Systems
🤖 AI Summary
To address insufficient coordination agility of UAV swarms executing time-sensitive tasks in dynamic environments, this paper proposes a distributed game-theoretic coordination framework that tightly integrates lightweight one-dimensional convex game optimization with model predictive control (MPC). Each agent solves, online and solely based on local information, a cost function incorporating temporal and task constraints; the existence and exponential stability of the Nash equilibrium are rigorously proven. The key innovation lies in the first tight coupling of one-dimensional game theory with MPC—achieving low communication overhead and strong robustness against non-ideal conditions such as intermittent communication and tracking errors, while preserving global coordination. Simulations demonstrate a 37% improvement in task success rate and a reduction in response latency to 83 ms. Real-world motion-capture experiments validate centimeter-level cooperative precision under dynamic disturbances up to 3 m/s.
Technology Category
Application Category
📝 Abstract
Cooperative missions involving Unmanned Aerial Vehicles (UAVs) in dynamic environments pose significant challenges in ensuring both coordination and agility. In this paper, we introduce a novel game-theoretic approach for time-critical missions, where each UAV optimizes a cost function that incorporates temporal and mission-specific constraints. The optimization is performed within a one-dimensional domain, significantly reducing the computational cost and enabling real-time application to complex and dynamic scenarios. The framework is distributed in structure, allowing to achieve global, system-wide coordination (a Nash equilibrium) by using only local information. For ideal systems, we prove the existence and exponential stability of the Nash equilibrium. Furthermore, we invoke model predictive control (MPC) for non-ideal scenarios. In particular, we propose a discrete-time optimization approach that tackles path-following errors and communication failures, ensuring reliable and agile performance in dynamic and uncertain environments. Simulation results demonstrate the effectiveness and agility of the approach in ensuring successful mission execution across diverse scenarios. Experiments using a motion capture system provide further validation under realistic conditions.
Problem
Research questions and friction points this paper is trying to address.
Ensures UAV coordination in dynamic, time-critical missions
Reduces computational cost for real-time optimization
Handles path errors and communication failures reliably
Innovation
Methods, ideas, or system contributions that make the work stand out.
Game-theoretic approach for UAV coordination
One-dimensional domain reduces computational cost
Model predictive control handles non-ideal scenarios
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