用深度强化学习让无人机群高效搜寻追踪地面目标
Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance
- 中心化架构下多智能体协同，各无人机由专用强化学习模型控制
- 仿真结果表明系统能快速定位目标并持续稳定跟踪
- 适合安防与执法场景的无人机群智能监控系统
本文深入总结了空中蜂群技术的最新进展，涵盖经典方法与基于强化学习的新范式。提出一种混合人工智能系统，采用多智能体集中式蜂群架构，集成深度强化学习实现地面目标的侦察与追踪。蜂群由中央控制器分配搜索与追踪任务，各无人机代理通过多个协作子代理进行控制，子代理行为分别使用近端策略优化（PPO）算法训练，适配不同任务类型。同时定义了评估蜂群性能的多项指标。仿真结果表明，该系统能有效搜索作业区域，合理时间内捕获目标，并实现连续稳定的跟踪。
原文摘要 · Abstract (English)
This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications. The swarm is governed by a central swarm controller responsible for distributing different search and tracking tasks among the cooperating UAVs. Each UAV agent is then controlled by a collection of cooperative sub-agents, whose behaviors have been trained using different deep reinforcement learning models, tailored for the different task types proposed by the swarm controller. More specifically, proximal policy optimization (PPO) algorithms were used to train the agents' behavior. In addition, several metrics to assess the performance of the swarm in this application were defined. The results obtained through simulation show that our system searches the operation area effectively, acquires the targets in a reasonable time, and is capable of tracking them continuously and consistently.
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