Efficient Guidance Control of Large-Scale Drone Swarms Using Implicitly Informed Agents

Abstract

Guidance control is crucial for the effective operation of distributed aerial swarm systems. Despite significant advancements, developing effective guidance control techniques for large-scale drone swarms remains a considerable challenge, especially when dealing with scenarios involving implicitly informed agents. Traditional methods often lead to swarm fragmentation in non-uniform guidance scenarios. Inspired by the intelligent swarming behavior of starlings, we propose a novel resilient guidance control model for drone swarms. This model employs a stochastic transition strategy for interaction modes based on a Markov decision process, establishing a swarm resilience mechanism that dynamically couples swarm cohesion with individual integration behaviors. This mechanism enables informed agents to guide individuals beyond their immediate topological interaction range effectively. Furthermore, the guidance control problem is formulated as a multi-objective optimization problem, which balances system flexibility and motion consistency through an adaptive optimization algorithm. Extensive simulations demonstrate that the model can guide a 1000-drone swarm to execute complex trajectories with a 99.95% success rate, even with only 5% informed agents. Real-world experiments using Crazyflie micro quadrotors further validate the model's practicality. This letter introduces a novel approach to large-scale drone swarm guidance and offers valuable insights for designing next-generation intelligent swarm systems.