Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue

用于搜索与救援的自主无人机集群智能三层学习架构

Abstract: This paper presents a novel three level hierarchical learning architecture for autonomous UAV swarms performing search and rescue operations. Unlike conventional approaches that apply a single learning paradigm across all hierarchy levels, the proposed architecture integrates three qualitatively different learning mechanisms corresponding to the biological hierarchy of reflexes, skills, and reasoning such as Hebbian neuroplasticity for individual agent adaptation, multi agent reinforcement learning with graph neural networks and behavior trees for tactical coordination, and model agnostic meta learning with BDI reasoning and a digital twin for strategic decision making.

摘要： 本文提出了一种用于执行搜索与救援任务的自主无人机集群的新型三层分层学习架构。与在所有层级应用单一学习范式的传统方法不同，该架构整合了三种性质迥异的学习机制，分别对应生物学中的反射、技能和推理层级：例如用于个体智能体适应的赫布神经可塑性（Hebbian neuroplasticity）、结合图神经网络与行为树的用于战术协调的多智能体强化学习（MARL），以及结合 BDI 推理与数字孪生的用于战略决策的模型无关元学习（Model-agnostic meta-learning）。

The architecture is formalized through twenty two architectural contracts organized across six components such as BDI, Behavior Trees, GNN, MARL, Neuroplasticity, Meta Learning that collectively provide six classes of formal guarantees such as safety, budget correctness, optimality, liveness, starvation freedom, and inter level consistency. We introduce Swarm Meta Cognition as a compositional property arising from the structured interaction of all three levels, enabling the swarm to monitor its own cognitive state and switch between cognitive strategies.

该架构通过分布在 BDI、行为树、GNN、MARL、神经可塑性和元学习这六个组件中的 22 项架构契约进行形式化，共同提供了六类形式化保证，包括安全性、预算正确性、最优性、活性、无饥饿性以及层间一致性。我们引入了“集群元认知”（Swarm Meta Cognition）作为一种由所有三个层级结构化交互产生的组合属性，使集群能够监控自身的认知状态并在不同认知策略之间进行切换。

Five constructive progress functions for SAR task types bridge the gap between abstract optimization theory and concrete operational scenarios. The main integration theorem establishes that when all contracts are satisfied, the hybrid neuro-symbolic system preserves all six guarantee classes. For the dynamic case with active learning, five new contracts extend the framework with three additional guarantees such as cognitive resilience, graceful degradation, and monotonic meta improvement. Theoretical analysis demonstrates that the architecture addresses five fundamental limitations of existing hierarchical RL approaches.

针对搜索与救援（SAR）任务类型的五种建设性进度函数，架起了抽象优化理论与具体操作场景之间的桥梁。主要集成定理证明，当所有契约得到满足时，该混合神经符号系统能够保持所有六类保证。对于涉及主动学习的动态情况，五项新契约扩展了该框架，增加了认知弹性、优雅降级和单调元改进这三项额外保证。理论分析表明，该架构解决了现有分层强化学习方法存在的五个根本性局限。