Autonomous aerial-surface robot teams are promising for maritime monitoring. Robust deployment requires reliable perception over reflective water and scalable coordination under limited communication. We present a decentralized multi-robot framework for detecting and tracking floating containers using multiple UAVs cooperating with an autonomous surface vessel. Each UAV performs YOLOv8 and stereo-disparity-based visual detection, then tracks targets with per-object EKFs using uncertainty-aware data association. Compact track summaries are exchanged and fused conservatively via covariance intersection, ensuring consistency under unknown correlations. An information-driven assignment module allocates targets and selects UAV hover viewpoints by trading expected uncertainty reduction against travel effort and safety separation. Simulation results in a maritime scenario demonstrate improved coverage, localization accuracy, and tracking consistency while maintaining modest communication requirements.

翻译：自主空中-水面机器人集群在海上监测领域具有广阔应用前景。实现稳健部署需要克服水面反光条件下的可靠感知挑战，并在有限通信条件下实现可扩展的协同作业。本文提出一种去中心化多机器人框架，利用多架无人机与自主水面艇协同实现漂浮集装箱的检测与跟踪。每架无人机采用YOLOv8结合立体视差的视觉检测方法，通过不确定性感知数据关联为每个目标建立扩展卡尔曼滤波器进行跟踪。系统通过协方差交集法对紧凑的轨迹摘要进行保守式交换与融合，确保在未知相关性条件下保持一致性。信息驱动的分配模块通过权衡预期不确定性降低、航行能耗与安全间距，实现目标分配并选择无人机悬停观测点。海上场景仿真结果表明，该系统在保持适度通信需求的同时，显著提升了监测覆盖率、定位精度与跟踪一致性。