Autonomous aerial-surface robot teams offer a scalable solution for maritime monitoring, but deployment remains difficult due to water-induced visual artifacts and bandwidth-limited coordination. This paper presents a decentralized multi-robot framework to detect and track floating containers using multiple UAVs cooperating with an autonomous surface vessel. Each UAV runs a YOLOv8 detector augmented with stereo disparity and maintains per-target EKF tracks with uncertainty-aware data association. Robots exchange compact track summaries that are fused conservatively using Covariance Intersection, preserving estimator consistency under unknown cross-correlations. An information-driven allocator assigns targets and selects UAV hover viewpoints by trading expected uncertainty reduction in travel effort and safety separation. Implemented in ROS, the proposed system is validated in simulations and compared with representative tracking and fusion baselines, showing improved identity continuity and localization accuracy with modest communication overhead.

翻译：自主空中-水面机器人编队为海上监测提供了可扩展的解决方案，但由于水体引起的视觉伪影和带宽受限的协同问题，其实际部署仍面临困难。本文提出一种分散式多机器人框架，利用多架无人机与自主水面艇协同工作，实现对漂浮集装箱的检测与跟踪。每架无人机运行基于立体视差增强的YOLOv8检测器，并通过不确定性感知数据关联为每个目标维持扩展卡尔曼滤波器轨迹。机器人间交换紧凑的轨迹摘要，并采用协方差交集法进行保守融合，在未知互相关条件下保持估计器的一致性。信息驱动的分配器通过权衡预期不确定性降低量与航行能耗及安全间距，为目标分配任务并选择无人机悬停观测点位。该系统基于ROS实现，通过仿真验证并与代表性跟踪融合基线方法进行对比，结果表明在适度通信开销下，该系统能提升身份连续性与定位精度。