发表机构
Northwestern Polytechnical University; Wuhan University; Nankai University（西北工业大学; 武汉大学; 南开大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出通用协作智能的统一综述，通过五维分类法和三个认知协同条件整合多智能体协作研究，并推出GCI-Bench基准协议以促进跨研究比较，推动现实不确定性下的通用协作智能发展。
AI 中文摘要
多智能体无人系统正从孤立的、以自我为中心的感知向协作智能转变，其中分布式智能体交换紧凑特征以克服任何单个智能体都无法逃脱的局部观测困境：遮挡、有限的传感器范围和环境退化。该领域已在架构、通信、具身、韧性和信任等维度上趋于成熟，然而现有综述孤立地审视这些维度，很少揭示它们之间的依赖关系。本综述通过两个互补的视角提供统一的综合。第一个是涵盖协作阶段、通信范式、融合架构、学习策略和应用领域的五维分类法。第二个是三个认知协同条件，即语义消歧、语用信息交换和主动信息觅食，这些条件将认知协同转化为操作标准。在这些视角下，我们考察了协作架构与拓扑、将信道视为可微管道组件的神经-通信协同设计、通过多智能体强化学习实现的具身行动-感知循环，以及用于同步、不确定性量化和标签高效学习的韧性机制。然后，我们将这些进展映射到四个操作领域，即车联万物（V2X）、无人机、工业物流和智慧城市，以及安全-隐私-效用三元组。为了应对基准饱和和评估碎片化，我们提出了GCI-Bench，一个包含成熟度模型的五支柱评分协议，使协作方法的权衡在不同研究之间具有可比性。对可复现性、仿真到现实的鸿沟以及协作降低性能的条件的批判性反思，识别了开放挑战，并指明了在现实世界不确定性下迈向通用协作智能的方向。
英文摘要
Multi-agent unmanned systems are moving from isolated, ego-centric sensing toward collaborative intelligence, in which distributed agents exchange compact features to overcome a local observation trap that no single agent can escape: occlusions, finite sensor range, and environmental degradation. The field has matured across architectural, communication, embodied, resilience, and trust dimensions, yet existing surveys examine these dimensions in isolation and rarely expose their dependencies. This review offers a unified synthesis through two complementary lenses. The first is a five-dimensional taxonomy spanning collaboration stage, communication paradigm, fusion architecture, learning strategy, and application domain. The second is three cognitive synergy conditions, Semantic Disambiguation, Pragmatic Information Exchange, and Proactive Informational Foraging, that turn cognitive synergy into operational criteria. Across these lenses we survey collaboration architectures and topologies, neural-communication co-design that treats the channel as a differentiable pipeline component, embodied action-perception loops via multi-agent reinforcement learning, and resilience mechanisms for synchronization, uncertainty quantification, and label-efficient learning. We then map these advances onto four operational domains, V2X, unmanned aerial, industrial logistics, and smart cities, and onto the safety-privacy-utility triad. To counter benchmark saturation and evaluation fragmentation, we propose GCI-Bench, a five-pillar scoring protocol with a maturity model that makes the trade-offs of collaborative methods comparable across studies. A critical reflection on reproducibility, the sim-to-real gulf, and conditions under which collaboration degrades performance identifies open challenges and charts directions toward general collaborative intelligence under real-world uncertainty.
